Recession Indicators

Introduction

For one of my machine learning classes we had a project that consumed financial data. I have extended that project to use machine learning to see if an indicator, or predictor, can be found that identifies market tops that occur prior to recessions. Then I use the model to build a trading strategy and backtest it to see how it performs.

Get Economic and Financial Data

Acquiring the data consists of two steps. First the code pulls the data into zoo objects which are then collapsed into a single data frame (df.data). Features are extracted from these series and added to the df.data data frame.

Sample call to pull economic data

Data is pulled from several sources include FRED, yahoo, and Google. The code below shows an example that pulls in the consumer price index (CPI) from the FRED. I pull data using quantmod, Quandl, and some manual extractions stored in spreadsheets.

# Consumer Price Index for All Urban Consumers: All Items
if (bRefresh == TRUE) {
  getSymbols("CPIAUCSL", src = "FRED", auto.assign = TRUE)
}

## [1] "CPIAUCSL"

## Warning: ASDAX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## Warning: ^TNX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## Warning: CL=F contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## Warning: ^IRX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.

## Warning: ^STOXX50E contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

Load up the EIA data

## Warning in .getMonEIA(ID, key = key): NAs introduced by coercion

## Warning in .getMonEIA(ID, key = key): NAs introduced by coercion

Load rig count data

The Baker Hughes rig count numbers

USDA data

Loading in farm data

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in E3 / R3C5: got a date

## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...

## Warning: NAs introduced by coercion

Loading in Silverblatt’s S&P 500 spreadsheet starting with the quarterly data.

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7

Now load in the estimates

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...

Covid 19 Data

Get the Covid-19 data from JHU

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   province = col_character(),
##   country = col_character(),
##   lat = col_double(),
##   long = col_double(),
##   type = col_character(),
##   cases = col_double()
## )

## Downloading GitHub repo RamiKrispin/coronavirus@master

##   
  
  
v  checking for file 'C:\Users\Rainy\AppData\Local\Temp\RtmpgPVUV1\remotes711040ad27c6\RamiKrispin-coronavirus-aced7f2/DESCRIPTION'
## 
  
  
  
-  preparing 'coronavirus': (579ms)
##    checking DESCRIPTION meta-information ...
  
   checking DESCRIPTION meta-information ... 
  
v  checking DESCRIPTION meta-information
## 
  
  
  
-  checking for LF line-endings in source and make files and shell scripts
## 
  
  
  
-  checking for empty or unneeded directories
## 
  
  
  
-  looking to see if a 'data/datalist' file should be added
## 
  
  
  
-  building 'coronavirus_0.3.3.tar.gz'
## 
  
   
## 

## Installing package into 'C:/Users/Rainy/Documents/R/win-library/3.6'
## (as 'lib' is unspecified)

## `summarise()` ungrouping output (override with `.groups` argument)

## `summarise()` regrouping output by 'country' (override with `.groups` argument)

## Warning: The `i` argument of ``[.tbl_df`()` must lie in [0, rows] if positive, as of tibble 3.0.0.
## Use `NA` as row index to obtain a row full of `NA` values.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

## Warning: Removed 2 row(s) containing missing values (geom_path).

Feature Extraction

With the raw data downloaded, some of the interesting features can be extracted. The first step is reconcile the time intervals. Some of the data is released monthly and some daily. I chose to interpolate all data to a daily interval. The first section of code adds the daily rows to the dataframe.

The code performs interpolation for continuous data or carries it forward for binary data like the recession indicators.

source("calcInterpolate.r")
df.data <- calcInterpolate(df.data, df.symbols)

## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

Truncate data

Create aggregate series

Some analysis requires that two or more series be combined. For example, normallizing debt by GDP to get a sense of the proportion of debt to the total economy helps understand the debt cycle.

Year over year, smoothed derivative, and log trends tend to smooth out seasonal variation. It gets used so often that I do this for every series downloaded.

source("calcFeatures.r")
lst.df <- calcFeatures(df.data, df.symbols)

## [1] "USREC has zero or negative values. Log series will be zero."
## [1] "GSFTX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMIX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMCX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMAX.Volume has zero or negative values. Log series will be zero."
## [1] "LCSIX.Volume has zero or negative values. Log series will be zero."
## [1] "VBIRX.Volume has zero or negative values. Log series will be zero."
## [1] "VFSUX.Volume has zero or negative values. Log series will be zero."
## [1] "LTUIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTPX.Volume has zero or negative values. Log series will be zero."
## [1] "NERYX.Volume has zero or negative values. Log series will be zero."
## [1] "STIGX.Volume has zero or negative values. Log series will be zero."
## [1] "HLGAX.Volume has zero or negative values. Log series will be zero."
## [1] "FTRGX.Volume has zero or negative values. Log series will be zero."
## [1] "THIIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTRX.Volume has zero or negative values. Log series will be zero."
## [1] "BFIGX.Volume has zero or negative values. Log series will be zero."
## [1] "EIFAX.Volume has zero or negative values. Log series will be zero."
## [1] "ASDAX.Volume has zero or negative values. Log series will be zero."
## [1] "TRBUX.Volume has zero or negative values. Log series will be zero."
## [1] "PRWCX.Volume has zero or negative values. Log series will be zero."
## [1] "ADOZX.Volume has zero or negative values. Log series will be zero."
## [1] "MERFX.Volume has zero or negative values. Log series will be zero."
## [1] "CMNIX.Volume has zero or negative values. Log series will be zero."
## [1] "CIHEX.Volume has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB has zero or negative values. Log series will be zero."
## [1] "TNX.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Open has zero or negative values. Log series will be zero."
## [1] "CLF.Low has zero or negative values. Log series will be zero."
## [1] "CLF.Close has zero or negative values. Log series will be zero."
## [1] "CLF.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Adjusted has zero or negative values. Log series will be zero."
## [1] "DTB3 has zero or negative values. Log series will be zero."
## [1] "IRX.Open has zero or negative values. Log series will be zero."
## [1] "IRX.High has zero or negative values. Log series will be zero."
## [1] "IRX.Low has zero or negative values. Log series will be zero."
## [1] "IRX.Close has zero or negative values. Log series will be zero."
## [1] "IRX.Volume has zero or negative values. Log series will be zero."
## [1] "IRX.Adjusted has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO has zero or negative values. Log series will be zero."
## [1] "RLG.Volume has zero or negative values. Log series will be zero."
## [1] "STOXX50E.Volume has zero or negative values. Log series will be zero."
## [1] "GDPNOW has zero or negative values. Log series will be zero."
## [1] "W790RC1Q027SBEA has zero or negative values. Log series will be zero."
## [1] "VXX.Volume has zero or negative values. Log series will be zero."
## [1] "FYFSD has zero or negative values. Log series will be zero."
## [1] "FYFSGDA188S has zero or negative values. Log series will be zero."
## [1] "SOFR25 has zero or negative values. Log series will be zero."
## [1] "SOFR1 has zero or negative values. Log series will be zero."
## [1] "RPONTSYD has zero or negative values. Log series will be zero."
## [1] "BOPGTB has zero or negative values. Log series will be zero."
## [1] "EES.Volume has zero or negative values. Log series will be zero."
## [1] "VGSTX.Volume has zero or negative values. Log series will be zero."
## [1] "VFINX.Volume has zero or negative values. Log series will be zero."
## [1] "TMFGX.Volume has zero or negative values. Log series will be zero."
## [1] "HAINX.Volume has zero or negative values. Log series will be zero."
## [1] "IVOO.Volume has zero or negative values. Log series will be zero."
## [1] "VO.Volume has zero or negative values. Log series will be zero."
## [1] "CZA.Volume has zero or negative values. Log series will be zero."
## [1] "SLY.Volume has zero or negative values. Log series will be zero."
## [1] "HYMB.Volume has zero or negative values. Log series will be zero."
## [1] "GOLD.Open has zero or negative values. Log series will be zero."
## [1] "GOLD.Volume has zero or negative values. Log series will be zero."
## [1] "BKR.Open has zero or negative values. Log series will be zero."
## [1] "BKR.Volume has zero or negative values. Log series will be zero."
## [1] "HAL.Open has zero or negative values. Log series will be zero."
## [1] "HAL.Volume has zero or negative values. Log series will be zero."
## [1] "IP.Open has zero or negative values. Log series will be zero."
## [1] "T.Open has zero or negative values. Log series will be zero."
## [1] "OPEARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "AREARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "OCCEquityVolume has zero or negative values. Log series will be zero."
## [1] "OCCNonEquityVolume has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA.by.GDP has zero or negative values. Log series will be zero."
## [1] "EXPCH.minus.IMPCH has zero or negative values. Log series will be zero."
## [1] "EXPMX.minus.IMPMX has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB.by.GDP has zero or negative values. Log series will be zero."
## [1] "DGS30TO10 has zero or negative values. Log series will be zero."
## [1] "DGS10TO1 has zero or negative values. Log series will be zero."
## [1] "DGS10TO2 has zero or negative values. Log series will be zero."
## [1] "DGS10TOTB3MS has zero or negative values. Log series will be zero."
## [1] "DGS10TODTB3 has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO.by.PPIACO has zero or negative values. Log series will be zero."
## [1] "GSPC.DailySwing has zero or negative values. Log series will be zero."

df.data <- lst.df[[1]]
df.symbols <- lst.df[[2]]

Recession calculations

Summary calculations

These values are used below

Conclusion

In this worksheet a model predicting the onset of recession was built. From the model a trading rule was derived to allow backtesting. The model performed well and the trading rule backtesting showed that applying this in the post-WWII period would have resulted in an increase in returns. That is not too bad, but there are a few changes that would likely improve the model:

• Go long on short term bonds, rather than just roll out of the market. That way at least some returns would be generated during recessions.
• Refine the recession indicator.

Market Conditions

#The model is predicting a `r paste(sprintf("%3.0f", tail(df.data$recession.initiation.smooth.avg,1)[[1]]*100), "%", sep="")` chance of recession in the next 12 months. :

#- P/E ratio of `r sprintf("%3.2f", tail(df.data$MULTPLSP500PERATIOMONTH,1))` compares to a historical mean value over the last decade of `r sprintf("%3.2f", df.data$MULTPLSP500PERATIOMONTH_Mean[1])`. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

As of Feb 2020 we have entered a recession as defined by the NBER yet the market continues to rise.

P/E ratio of 35.29 compares to a historical mean value over the last decade of 18.57. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

• S&P 500 Volume, last updated on 2021-09-09, is negative over the last year and positive over the last month.

Unemployment

• Headline unemployment (U-3) stands at 5.20% (last updated on 2021-08-01) which is near the 1-year average of 6.08% and rising with respect to the low in the last twelve months of 5.20%. Unlikely the rate will drop again.

• Payrolls (BLS data, NSA) year-over-year stands at 3.14% which is above the 1-year average of 0.21% and falling with respect to the peak, in the last twelve months, of 10.86%.

• Jobless claims (ICSA data) year-over-year stands at -64.33% (last updated on 2021-09-04) which is in-line with the 1-year average of 102.35% and below the peak, in the last twelve months, of 337.62%.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_hline).

Personal Income

• Real personal income year over year growth stands at 1.36% (last updated on 2021-07-01). This is below the recent peak of 7.97%.

Yield Curve and Bond Market

• The 10-year to 3-month yield stands at 1.30% (last updated on 2021-09-08). This is above the recent low of 0.56%. The trend is positive over the last year and positive over the last month.

• Auto sales flat?

Auxillary Series

I explored additional data series. The sections below have those data series along with comments.

Recent Highs

Print out the new 180 day high values

df.symbolsTrue <-
  df.symbols[df.symbols$'Max180' == TRUE, c("string.symbol", "string.description")]
df.symbolsTrue <-
  df.symbolsTrue[!(is.na(df.symbolsTrue$string.symbol)), ]
df.symbolsTrue <-
  df.symbolsTrue[!(df.symbolsTrue$string.symbol == 'USREC'), ]
#print(head(df.symbolsTrue,20))

kable(df.symbolsTrue, caption = "6-Month High") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))  

6-Month High

	string.symbol	string.description
1	CPIAUCSL	Consumer Price Index for All Urban Consumers: All Items
4	PCEPI	Personal Consumption Expenditures: Chain-type Price Index
7	NPPTTL	Total Nonfarm Private Payroll Employment (ADP)
9	PAYNSA	All Employees: Total Nonfarm Payrolls (NSA)
10	TABSHNO	Households and nonprofit organizations; total assets, Level
11	HNONWPDPI	Household Net Worth, percent Dispsable Income
12	INDPRO	Industrial Production Index
14	RSALES	Real Retail Sales (DISCONTINUED)
15	W875RX1	Real personal income excluding current transfer receipts
49	IMPCH	U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
54	HNFSUSNSA	New One Family Houses for Sale in the United States (Monthly, NSA)
58	REALLNNSA	Real Estate Loans, All Commercial Banks (Monthly, NSA)
59	REALLN	Real Estate Loans, All Commercial Banks (Monthly, SA)
61	RELACBW027SBOG	Real Estate Loans, All Commercial Banks (Weekly, SA)
62	RREACBM027NBOG	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
64	RREACBW027SBOG	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
67	CONSUMERNSA	Consumer Loans, All Commercial Banks
69	DPSACBW027SBOG	Deposits, All Commercial Banks
72	SRPSABSNNCB	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
73	ASTLL	All sectors; total loans; liability, Level (NSA)
74	FBDILNECA	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
75	ASOLAL	All sectors; other loans and advances; liability, Level (NSA)
76	ASTMA	All sectors; total mortgages; asset, Level (NSA)
77	ASHMA	All sectors; home mortgages; asset, Level (NSA)
78	ASMRMA	All sectors; multifamily residential mortgages; asset, Level (NSA)
79	ASCMA	All sectors; commercial mortgages; asset, Level (NSA)
80	ASFMA	All sectors; farm mortgages; asset, Level (NSA)
81	CCLBSHNO	Households and nonprofit organizations; consumer credit; liability, Level (NSA)
82	FBDSILQ027S	Domestic financial sectors debt securities; liability, Level (NSA)
83	FBLL	Domestic financial sectors loans; liability, Level (NSA)
84	NCBDBIQ027S	Nonfinancial corporate business; debt securities; liability, Level
91	TB3MS	3-Month Treasury Bill: Secondary Market Rate (Monthly)
96	NEWORDER	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
104	GDP	Gross Domestic Product
106	FDEFX	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
108	GDPC1	Real Gross Domestic Product
109	GDPDEF	Gross Domestic Product: Implicit Price Deflator
111	WLRRAL	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
115	MZMV	Velocity of MZM Money Stock
116	M1	M1 Money Stock
117	M2	M2 Money Stock
118	OPHNFB	Nonfarm Business Sector: Real Output Per Hour of All Persons
119	IPMAN	Industrial Production: Manufacturing (NAICS)
126	GFDEBTN	Federal Debt: Total Public Debt
128	MSPUS	Median Sales Price of Houses Sold for the United States
131	CSUSHPINSA	S&P/Case-Shiller U.S. National Home Price Index (NSA)
133	FYFSD	Federal Surplus or Deficit
134	FYFSGDA188S	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
139	WALCL	All Federal Reserve Banks: Total Assets
140	OUTMS	Manufacturing Sector: Real Output
141	MANEMP	All Employees: Manufacturing
142	PRS30006163	Manufacturing Sector: Real Output Per Person
145	SOFR	Secured Overnight Financing Rate
147	SOFR99	Secured Overnight Financing Rate: 99th Percentile
149	SOFR25	Secured Overnight Financing Rate: 25th Percentile
157	IOER	Interest Rate on Excess Reserves
158	WRESBAL	Reserve Balances with Federal Reserve Banks
159	EXCSRESNW	Excess Reserves of Depository Institutions
160	ECBASSETS	Central Bank Assets for Euro Area (11-19 Countries)
161	EUNNGDP	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
162	CEU0600000007	Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
164	CURRENCY	Currency Component of M1 (Seasonally Adjusted)
165	WCURRNS	Currency Component of M1
166	BOGMBASE	Monetary Base; Total
167	PRS88003193	Nonfinancial Corporations Sector: Unit Profits
168	PPIACO	Producer Price Index for All Commodities
169	PCUOMFGOMFG	Producer Price Index by Industry: Total Manufacturing Industries
170	POPTHM	Population (U.S.)
171	POPTHM	Population (U.S.)
172	CLF16OV	Civilian Labor Force Level, SA
179	TERMCBPER24NS	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
180	A065RC1A027NBEA	Personal income (NSA)
182	PCE	Personal Consumption Expenditures (SA)
183	A053RC1Q027SBEA	National income: Corporate profits before tax (without IVA and CCAdj)
184	CPROFIT	Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
219	MULTPLSP500SALESQUARTER	S&P 500 TTM Sales (Not Inflation Adjusted)
223	WWDIWLDISAIRGOODMTK1	Air transport, freight
226	PETA143B00001M	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
228	TOTALOGNRPUSM	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly
229	TOTALPANRPUSM	Crude Oil Rotary Rigs in Operation, Monthly
230	TOTALNGNRPUSM	Natural Gas Rotary Rigs in Operation, Monthly
231	BKRTotal	Total Rig Count
232	BKRGas	Gas Rig Count
233	BKROil	Oil Rig Count
234	FARMINCOME	Net Farm Income
235	OPEARNINGSPERSHARE	Operating Earnings per Share
236	AREARNINGSPERSHARE	As-Reported Earnings per Share
237	CASHDIVIDENDSPERSHR	Cash Dividends per Share
239	BOOKVALPERSHR	Book value per Share
241	PRICE	Price
242	OPEARNINGSTTM	TTM Operating Earnings
243	AREARNINGSTTM	TTM Reported Earnings
246	OCCEquityVolume	Equity Options Volume
247	OCCNonEquityVolume	Non-Equity Options Volume
249	BUSLOANS.minus.BUSLOANSNSA	Business Loans (Montlhy) SA - NSA
250	BUSLOANS.minus.BUSLOANSNSA.by.GDP	Business Loans (Montlhy) SA - NSA divided by GDP
262	A053RC1Q027SBEA.by.GDP	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
263	CPROFIT.by.GDP	National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
264	CONSUMERNSA.by.GDP	Consumer Loans Not Seasonally Adjusted divided by GDP
274	CONSUMERNSA.INTEREST	Consumer Loans (Not Seasonally Adjusted) Interest Burdens
275	CONSUMERNSA.INTEREST.by.GDP	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
280	WRESBAL.by.GDP	Reserve Balances with Federal Reserve Banks Divided by GDP
282	WLRRAL.by.GDP	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
289	ASFMA.by.ASTLL	All sectors; total loans Divided by farm mortgages
293	BOGMBASE.by.GDP	BOGMBASE Divided by GDP
294	WALCL.by.GDP	All Federal Reserve Banks: Total Assets Divided by GDP
304	NPPTTLBYPOPTHM	ADP Private Employment / Population
323	HNFSUSNSA.minus.HSN1FNSA	Houses for sale - houses sold
325	MSPUS.times.HNFSUSNSA	New privately owned 1-family units for sale times median price
331	CPIAUCSL_SmoothDer	Derivative of Smoothed Consumer Price Index for All Urban Consumers: All Items
332	CPIAUCSL_Log	Log of Consumer Price Index for All Urban Consumers: All Items
333	CPIAUCSL_mva200	Consumer Price Index for All Urban Consumers: All Items 200 Day MA
334	CPIAUCSL_mva050	Consumer Price Index for All Urban Consumers: All Items 50 Day MA
335	USREC_YoY	NBER based Recession Indicators Year over Year
336	USREC_YoY4	NBER based Recession Indicators 4 Year over 4 Year
337	USREC_YoY5	NBER based Recession Indicators 5 Year over 5 Year
338	USREC_Smooth	Savitsky-Golay Smoothed (p=3, n=365) NBER based Recession Indicators
339	USREC_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) NBER based Recession Indicators
341	USREC_Log	Log of NBER based Recession Indicators
342	USREC_mva200	NBER based Recession Indicators 200 Day MA
343	USREC_mva050	NBER based Recession Indicators 50 Day MA
359	PCEPI_Log	Log of Personal Consumption Expenditures: Chain-type Price Index
360	PCEPI_mva200	Personal Consumption Expenditures: Chain-type Price Index 200 Day MA
361	PCEPI_mva050	Personal Consumption Expenditures: Chain-type Price Index 50 Day MA
367	CCSA_SmoothDer	Derivative of Smoothed Continued Claims (Insured Unemployment)
376	CCNSA_SmoothDer	Derivative of Smoothed Continued Claims (Insured Unemployment, NSA)
383	NPPTTL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Total Nonfarm Private Payroll Employment (ADP)
385	NPPTTL_SmoothDer	Derivative of Smoothed Total Nonfarm Private Payroll Employment (ADP)
386	NPPTTL_Log	Log of Total Nonfarm Private Payroll Employment (ADP)
387	NPPTTL_mva200	Total Nonfarm Private Payroll Employment (ADP) 200 Day MA
388	NPPTTL_mva050	Total Nonfarm Private Payroll Employment (ADP) 50 Day MA
403	PAYNSA_SmoothDer	Derivative of Smoothed All Employees: Total Nonfarm Payrolls (NSA)
404	PAYNSA_Log	Log of All Employees: Total Nonfarm Payrolls (NSA)
405	PAYNSA_mva200	All Employees: Total Nonfarm Payrolls (NSA) 200 Day MA
406	PAYNSA_mva050	All Employees: Total Nonfarm Payrolls (NSA) 50 Day MA
410	TABSHNO_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Households and nonprofit organizations; total assets, Level
413	TABSHNO_Log	Log of Households and nonprofit organizations; total assets, Level
414	TABSHNO_mva200	Households and nonprofit organizations; total assets, Level 200 Day MA
415	TABSHNO_mva050	Households and nonprofit organizations; total assets, Level 50 Day MA
421	HNONWPDPI_SmoothDer	Derivative of Smoothed Household Net Worth, percent Dispsable Income
422	HNONWPDPI_Log	Log of Household Net Worth, percent Dispsable Income
424	HNONWPDPI_mva050	Household Net Worth, percent Dispsable Income 50 Day MA
428	INDPRO_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Industrial Production Index
430	INDPRO_SmoothDer	Derivative of Smoothed Industrial Production Index
431	INDPRO_Log	Log of Industrial Production Index
432	INDPRO_mva200	Industrial Production Index 200 Day MA
433	INDPRO_mva050	Industrial Production Index 50 Day MA
443	RSALES_YoY	Real Retail Sales (DISCONTINUED) Year over Year
444	RSALES_YoY4	Real Retail Sales (DISCONTINUED) 4 Year over 4 Year
445	RSALES_YoY5	Real Retail Sales (DISCONTINUED) 5 Year over 5 Year
449	RSALES_Log	Log of Real Retail Sales (DISCONTINUED)
450	RSALES_mva200	Real Retail Sales (DISCONTINUED) 200 Day MA
451	RSALES_mva050	Real Retail Sales (DISCONTINUED) 50 Day MA
455	W875RX1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real personal income excluding current transfer receipts
457	W875RX1_SmoothDer	Derivative of Smoothed Real personal income excluding current transfer receipts
458	W875RX1_Log	Log of Real personal income excluding current transfer receipts
459	W875RX1_mva200	Real personal income excluding current transfer receipts 200 Day MA
460	W875RX1_mva050	Real personal income excluding current transfer receipts 50 Day MA
477	PCOPPUSDM_mva200	Global price of Copper 200 Day MA
486	NOBL.Open_mva200	200 Day MA
487	NOBL.Open_mva050	50 Day MA
495	NOBL.High_mva200	200 Day MA
496	NOBL.High_mva050	50 Day MA
504	NOBL.Low_mva200	200 Day MA
505	NOBL.Low_mva050	50 Day MA
513	NOBL.Close_mva200	200 Day MA
514	NOBL.Close_mva050	50 Day MA
531	NOBL.Adjusted_mva200	200 Day MA
532	NOBL.Adjusted_mva050	50 Day MA
540	SCHD.Open_mva200	200 Day MA
541	SCHD.Open_mva050	50 Day MA
549	SCHD.High_mva200	200 Day MA
550	SCHD.High_mva050	50 Day MA
558	SCHD.Low_mva200	200 Day MA
559	SCHD.Low_mva050	50 Day MA
567	SCHD.Close_mva200	200 Day MA
568	SCHD.Close_mva050	50 Day MA
585	SCHD.Adjusted_mva200	200 Day MA
586	SCHD.Adjusted_mva050	50 Day MA
590	PFF.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
592	PFF.Open_SmoothDer	Derivative of Smoothed
594	PFF.Open_mva200	200 Day MA
595	PFF.Open_mva050	50 Day MA
599	PFF.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
601	PFF.High_SmoothDer	Derivative of Smoothed
603	PFF.High_mva200	200 Day MA
604	PFF.High_mva050	50 Day MA
608	PFF.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
610	PFF.Low_SmoothDer	Derivative of Smoothed
612	PFF.Low_mva200	200 Day MA
613	PFF.Low_mva050	50 Day MA
617	PFF.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
619	PFF.Close_SmoothDer	Derivative of Smoothed
621	PFF.Close_mva200	200 Day MA
622	PFF.Close_mva050	50 Day MA
635	PFF.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
637	PFF.Adjusted_SmoothDer	Derivative of Smoothed
639	PFF.Adjusted_mva200	200 Day MA
640	PFF.Adjusted_mva050	50 Day MA
646	HPI.Open_SmoothDer	Derivative of Smoothed
648	HPI.Open_mva200	200 Day MA
649	HPI.Open_mva050	50 Day MA
655	HPI.High_SmoothDer	Derivative of Smoothed
657	HPI.High_mva200	200 Day MA
658	HPI.High_mva050	50 Day MA
662	HPI.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
664	HPI.Low_SmoothDer	Derivative of Smoothed
666	HPI.Low_mva200	200 Day MA
667	HPI.Low_mva050	50 Day MA
673	HPI.Close_SmoothDer	Derivative of Smoothed
675	HPI.Close_mva200	200 Day MA
676	HPI.Close_mva050	50 Day MA
689	HPI.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
691	HPI.Adjusted_SmoothDer	Derivative of Smoothed
693	HPI.Adjusted_mva200	200 Day MA
694	HPI.Adjusted_mva050	50 Day MA
698	GSFTX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
702	GSFTX.Open_mva200	200 Day MA
703	GSFTX.Open_mva050	50 Day MA
707	GSFTX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
711	GSFTX.High_mva200	200 Day MA
712	GSFTX.High_mva050	50 Day MA
716	GSFTX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
720	GSFTX.Low_mva200	200 Day MA
721	GSFTX.Low_mva050	50 Day MA
725	GSFTX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
729	GSFTX.Close_mva200	200 Day MA
730	GSFTX.Close_mva050	50 Day MA
731	GSFTX.Volume_YoY	Year over Year
732	GSFTX.Volume_YoY4	4 Year over 4 Year
733	GSFTX.Volume_YoY5	5 Year over 5 Year
734	GSFTX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
735	GSFTX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
736	GSFTX.Volume_SmoothDer	Derivative of Smoothed
737	GSFTX.Volume_Log	Log of
738	GSFTX.Volume_mva200	200 Day MA
739	GSFTX.Volume_mva050	50 Day MA
743	GSFTX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
747	GSFTX.Adjusted_mva200	200 Day MA
748	GSFTX.Adjusted_mva050	50 Day MA
785	LFMIX.Volume_YoY	Year over Year
786	LFMIX.Volume_YoY4	4 Year over 4 Year
787	LFMIX.Volume_YoY5	5 Year over 5 Year
788	LFMIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
789	LFMIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
790	LFMIX.Volume_SmoothDer	Derivative of Smoothed
791	LFMIX.Volume_Log	Log of
792	LFMIX.Volume_mva200	200 Day MA
793	LFMIX.Volume_mva050	50 Day MA
839	LFMCX.Volume_YoY	Year over Year
840	LFMCX.Volume_YoY4	4 Year over 4 Year
841	LFMCX.Volume_YoY5	5 Year over 5 Year
842	LFMCX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
843	LFMCX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
844	LFMCX.Volume_SmoothDer	Derivative of Smoothed
845	LFMCX.Volume_Log	Log of
846	LFMCX.Volume_mva200	200 Day MA
847	LFMCX.Volume_mva050	50 Day MA
893	LFMAX.Volume_YoY	Year over Year
894	LFMAX.Volume_YoY4	4 Year over 4 Year
895	LFMAX.Volume_YoY5	5 Year over 5 Year
896	LFMAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
897	LFMAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
898	LFMAX.Volume_SmoothDer	Derivative of Smoothed
899	LFMAX.Volume_Log	Log of
900	LFMAX.Volume_mva200	200 Day MA
901	LFMAX.Volume_mva050	50 Day MA
913	LCSIX.Open_YoY5	5 Year over 5 Year
916	LCSIX.Open_SmoothDer	Derivative of Smoothed
917	LCSIX.Open_Log	Log of
918	LCSIX.Open_mva200	200 Day MA
919	LCSIX.Open_mva050	50 Day MA
922	LCSIX.High_YoY5	5 Year over 5 Year
925	LCSIX.High_SmoothDer	Derivative of Smoothed
926	LCSIX.High_Log	Log of
927	LCSIX.High_mva200	200 Day MA
928	LCSIX.High_mva050	50 Day MA
931	LCSIX.Low_YoY5	5 Year over 5 Year
934	LCSIX.Low_SmoothDer	Derivative of Smoothed
935	LCSIX.Low_Log	Log of
936	LCSIX.Low_mva200	200 Day MA
937	LCSIX.Low_mva050	50 Day MA
940	LCSIX.Close_YoY5	5 Year over 5 Year
943	LCSIX.Close_SmoothDer	Derivative of Smoothed
944	LCSIX.Close_Log	Log of
945	LCSIX.Close_mva200	200 Day MA
946	LCSIX.Close_mva050	50 Day MA
947	LCSIX.Volume_YoY	Year over Year
948	LCSIX.Volume_YoY4	4 Year over 4 Year
949	LCSIX.Volume_YoY5	5 Year over 5 Year
950	LCSIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
951	LCSIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
952	LCSIX.Volume_SmoothDer	Derivative of Smoothed
953	LCSIX.Volume_Log	Log of
954	LCSIX.Volume_mva200	200 Day MA
955	LCSIX.Volume_mva050	50 Day MA
958	LCSIX.Adjusted_YoY5	5 Year over 5 Year
962	LCSIX.Adjusted_Log	Log of
963	LCSIX.Adjusted_mva200	200 Day MA
964	LCSIX.Adjusted_mva050	50 Day MA
970	BSV.Open_SmoothDer	Derivative of Smoothed
979	BSV.High_SmoothDer	Derivative of Smoothed
988	BSV.Low_SmoothDer	Derivative of Smoothed
997	BSV.Close_SmoothDer	Derivative of Smoothed
1004	BSV.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1013	BSV.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1015	BSV.Adjusted_SmoothDer	Derivative of Smoothed
1024	VBIRX.Open_SmoothDer	Derivative of Smoothed
1033	VBIRX.High_SmoothDer	Derivative of Smoothed
1042	VBIRX.Low_SmoothDer	Derivative of Smoothed
1051	VBIRX.Close_SmoothDer	Derivative of Smoothed
1055	VBIRX.Volume_YoY	Year over Year
1056	VBIRX.Volume_YoY4	4 Year over 4 Year
1057	VBIRX.Volume_YoY5	5 Year over 5 Year
1058	VBIRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1059	VBIRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1060	VBIRX.Volume_SmoothDer	Derivative of Smoothed
1061	VBIRX.Volume_Log	Log of
1062	VBIRX.Volume_mva200	200 Day MA
1063	VBIRX.Volume_mva050	50 Day MA
1067	VBIRX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1069	VBIRX.Adjusted_SmoothDer	Derivative of Smoothed
1076	BIV.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1078	BIV.Open_SmoothDer	Derivative of Smoothed
1085	BIV.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1087	BIV.High_SmoothDer	Derivative of Smoothed
1094	BIV.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1096	BIV.Low_SmoothDer	Derivative of Smoothed
1103	BIV.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1105	BIV.Close_SmoothDer	Derivative of Smoothed
1121	BIV.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1123	BIV.Adjusted_SmoothDer	Derivative of Smoothed
1132	VFSUX.Open_SmoothDer	Derivative of Smoothed
1141	VFSUX.High_SmoothDer	Derivative of Smoothed
1150	VFSUX.Low_SmoothDer	Derivative of Smoothed
1159	VFSUX.Close_SmoothDer	Derivative of Smoothed
1163	VFSUX.Volume_YoY	Year over Year
1164	VFSUX.Volume_YoY4	4 Year over 4 Year
1165	VFSUX.Volume_YoY5	5 Year over 5 Year
1166	VFSUX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1167	VFSUX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1168	VFSUX.Volume_SmoothDer	Derivative of Smoothed
1169	VFSUX.Volume_Log	Log of
1170	VFSUX.Volume_mva200	200 Day MA
1171	VFSUX.Volume_mva050	50 Day MA
1175	VFSUX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1177	VFSUX.Adjusted_SmoothDer	Derivative of Smoothed
1179	VFSUX.Adjusted_mva200	200 Day MA
1186	LTUIX.Open_SmoothDer	Derivative of Smoothed
1195	LTUIX.High_SmoothDer	Derivative of Smoothed
1204	LTUIX.Low_SmoothDer	Derivative of Smoothed
1213	LTUIX.Close_SmoothDer	Derivative of Smoothed
1217	LTUIX.Volume_YoY	Year over Year
1218	LTUIX.Volume_YoY4	4 Year over 4 Year
1219	LTUIX.Volume_YoY5	5 Year over 5 Year
1220	LTUIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1221	LTUIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1222	LTUIX.Volume_SmoothDer	Derivative of Smoothed
1223	LTUIX.Volume_Log	Log of
1224	LTUIX.Volume_mva200	200 Day MA
1225	LTUIX.Volume_mva050	50 Day MA
1229	LTUIX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1231	LTUIX.Adjusted_SmoothDer	Derivative of Smoothed
1238	PTTPX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1240	PTTPX.Open_SmoothDer	Derivative of Smoothed
1247	PTTPX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1249	PTTPX.High_SmoothDer	Derivative of Smoothed
1256	PTTPX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1258	PTTPX.Low_SmoothDer	Derivative of Smoothed
1265	PTTPX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1267	PTTPX.Close_SmoothDer	Derivative of Smoothed
1271	PTTPX.Volume_YoY	Year over Year
1272	PTTPX.Volume_YoY4	4 Year over 4 Year
1273	PTTPX.Volume_YoY5	5 Year over 5 Year
1274	PTTPX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1275	PTTPX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1276	PTTPX.Volume_SmoothDer	Derivative of Smoothed
1277	PTTPX.Volume_Log	Log of
1278	PTTPX.Volume_mva200	200 Day MA
1279	PTTPX.Volume_mva050	50 Day MA
1283	PTTPX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1285	PTTPX.Adjusted_SmoothDer	Derivative of Smoothed
1292	NERYX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1294	NERYX.Open_SmoothDer	Derivative of Smoothed
1301	NERYX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1303	NERYX.High_SmoothDer	Derivative of Smoothed
1310	NERYX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1312	NERYX.Low_SmoothDer	Derivative of Smoothed
1319	NERYX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1321	NERYX.Close_SmoothDer	Derivative of Smoothed
1325	NERYX.Volume_YoY	Year over Year
1326	NERYX.Volume_YoY4	4 Year over 4 Year
1327	NERYX.Volume_YoY5	5 Year over 5 Year
1328	NERYX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1329	NERYX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1330	NERYX.Volume_SmoothDer	Derivative of Smoothed
1331	NERYX.Volume_Log	Log of
1332	NERYX.Volume_mva200	200 Day MA
1333	NERYX.Volume_mva050	50 Day MA
1337	NERYX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1339	NERYX.Adjusted_SmoothDer	Derivative of Smoothed
1346	STIGX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1348	STIGX.Open_SmoothDer	Derivative of Smoothed
1355	STIGX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1357	STIGX.High_SmoothDer	Derivative of Smoothed
1364	STIGX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1366	STIGX.Low_SmoothDer	Derivative of Smoothed
1373	STIGX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1375	STIGX.Close_SmoothDer	Derivative of Smoothed
1379	STIGX.Volume_YoY	Year over Year
1380	STIGX.Volume_YoY4	4 Year over 4 Year
1381	STIGX.Volume_YoY5	5 Year over 5 Year
1382	STIGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1383	STIGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1384	STIGX.Volume_SmoothDer	Derivative of Smoothed
1385	STIGX.Volume_Log	Log of
1386	STIGX.Volume_mva200	200 Day MA
1387	STIGX.Volume_mva050	50 Day MA
1391	STIGX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1393	STIGX.Adjusted_SmoothDer	Derivative of Smoothed
1400	HLGAX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1402	HLGAX.Open_SmoothDer	Derivative of Smoothed
1409	HLGAX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1411	HLGAX.High_SmoothDer	Derivative of Smoothed
1418	HLGAX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1420	HLGAX.Low_SmoothDer	Derivative of Smoothed
1427	HLGAX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1429	HLGAX.Close_SmoothDer	Derivative of Smoothed
1433	HLGAX.Volume_YoY	Year over Year
1434	HLGAX.Volume_YoY4	4 Year over 4 Year
1435	HLGAX.Volume_YoY5	5 Year over 5 Year
1436	HLGAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1437	HLGAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1438	HLGAX.Volume_SmoothDer	Derivative of Smoothed
1439	HLGAX.Volume_Log	Log of
1440	HLGAX.Volume_mva200	200 Day MA
1441	HLGAX.Volume_mva050	50 Day MA
1445	HLGAX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1447	HLGAX.Adjusted_SmoothDer	Derivative of Smoothed
1454	FTRGX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1456	FTRGX.Open_SmoothDer	Derivative of Smoothed
1463	FTRGX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1465	FTRGX.High_SmoothDer	Derivative of Smoothed
1472	FTRGX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1474	FTRGX.Low_SmoothDer	Derivative of Smoothed
1481	FTRGX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1483	FTRGX.Close_SmoothDer	Derivative of Smoothed
1487	FTRGX.Volume_YoY	Year over Year
1488	FTRGX.Volume_YoY4	4 Year over 4 Year
1489	FTRGX.Volume_YoY5	5 Year over 5 Year
1490	FTRGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1491	FTRGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1492	FTRGX.Volume_SmoothDer	Derivative of Smoothed
1493	FTRGX.Volume_Log	Log of
1494	FTRGX.Volume_mva200	200 Day MA
1495	FTRGX.Volume_mva050	50 Day MA
1499	FTRGX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1501	FTRGX.Adjusted_SmoothDer	Derivative of Smoothed
1508	THIIX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1510	THIIX.Open_SmoothDer	Derivative of Smoothed
1517	THIIX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1519	THIIX.High_SmoothDer	Derivative of Smoothed
1526	THIIX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1528	THIIX.Low_SmoothDer	Derivative of Smoothed
1535	THIIX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1537	THIIX.Close_SmoothDer	Derivative of Smoothed
1541	THIIX.Volume_YoY	Year over Year
1542	THIIX.Volume_YoY4	4 Year over 4 Year
1543	THIIX.Volume_YoY5	5 Year over 5 Year
1544	THIIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1545	THIIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1546	THIIX.Volume_SmoothDer	Derivative of Smoothed
1547	THIIX.Volume_Log	Log of
1548	THIIX.Volume_mva200	200 Day MA
1549	THIIX.Volume_mva050	50 Day MA
1553	THIIX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1555	THIIX.Adjusted_SmoothDer	Derivative of Smoothed
1562	PTTRX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1564	PTTRX.Open_SmoothDer	Derivative of Smoothed
1571	PTTRX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1573	PTTRX.High_SmoothDer	Derivative of Smoothed
1580	PTTRX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1582	PTTRX.Low_SmoothDer	Derivative of Smoothed
1589	PTTRX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1591	PTTRX.Close_SmoothDer	Derivative of Smoothed
1595	PTTRX.Volume_YoY	Year over Year
1596	PTTRX.Volume_YoY4	4 Year over 4 Year
1597	PTTRX.Volume_YoY5	5 Year over 5 Year
1598	PTTRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1599	PTTRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1600	PTTRX.Volume_SmoothDer	Derivative of Smoothed
1601	PTTRX.Volume_Log	Log of
1602	PTTRX.Volume_mva200	200 Day MA
1603	PTTRX.Volume_mva050	50 Day MA
1607	PTTRX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1609	PTTRX.Adjusted_SmoothDer	Derivative of Smoothed
1615	BFIGX.Open_YoY5	5 Year over 5 Year
1616	BFIGX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1617	BFIGX.Open_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1618	BFIGX.Open_SmoothDer	Derivative of Smoothed
1619	BFIGX.Open_Log	Log of
1621	BFIGX.Open_mva050	50 Day MA
1624	BFIGX.High_YoY5	5 Year over 5 Year
1625	BFIGX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1626	BFIGX.High_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1627	BFIGX.High_SmoothDer	Derivative of Smoothed
1628	BFIGX.High_Log	Log of
1630	BFIGX.High_mva050	50 Day MA
1633	BFIGX.Low_YoY5	5 Year over 5 Year
1634	BFIGX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1635	BFIGX.Low_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1636	BFIGX.Low_SmoothDer	Derivative of Smoothed
1637	BFIGX.Low_Log	Log of
1639	BFIGX.Low_mva050	50 Day MA
1642	BFIGX.Close_YoY5	5 Year over 5 Year
1643	BFIGX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1644	BFIGX.Close_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1645	BFIGX.Close_SmoothDer	Derivative of Smoothed
1646	BFIGX.Close_Log	Log of
1648	BFIGX.Close_mva050	50 Day MA
1649	BFIGX.Volume_YoY	Year over Year
1650	BFIGX.Volume_YoY4	4 Year over 4 Year
1651	BFIGX.Volume_YoY5	5 Year over 5 Year
1652	BFIGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1653	BFIGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1654	BFIGX.Volume_SmoothDer	Derivative of Smoothed
1655	BFIGX.Volume_Log	Log of
1656	BFIGX.Volume_mva200	200 Day MA
1657	BFIGX.Volume_mva050	50 Day MA
1660	BFIGX.Adjusted_YoY5	5 Year over 5 Year
1661	BFIGX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1662	BFIGX.Adjusted_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1663	BFIGX.Adjusted_SmoothDer	Derivative of Smoothed
1664	BFIGX.Adjusted_Log	Log of
1665	BFIGX.Adjusted_mva200	200 Day MA
1666	BFIGX.Adjusted_mva050	50 Day MA
1674	VTWO.Open_mva200	200 Day MA
1692	VTWO.Low_mva200	200 Day MA
1706	VTWO.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1719	VTWO.Adjusted_mva200	200 Day MA
1722	EIFAX.Open_YoY4	4 Year over 4 Year
1727	EIFAX.Open_Log	Log of
1731	EIFAX.High_YoY4	4 Year over 4 Year
1736	EIFAX.High_Log	Log of
1740	EIFAX.Low_YoY4	4 Year over 4 Year
1745	EIFAX.Low_Log	Log of
1749	EIFAX.Close_YoY4	4 Year over 4 Year
1754	EIFAX.Close_Log	Log of
1757	EIFAX.Volume_YoY	Year over Year
1758	EIFAX.Volume_YoY4	4 Year over 4 Year
1759	EIFAX.Volume_YoY5	5 Year over 5 Year
1760	EIFAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1761	EIFAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1762	EIFAX.Volume_SmoothDer	Derivative of Smoothed
1763	EIFAX.Volume_Log	Log of
1764	EIFAX.Volume_mva200	200 Day MA
1765	EIFAX.Volume_mva050	50 Day MA
1769	EIFAX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1772	EIFAX.Adjusted_Log	Log of
1773	EIFAX.Adjusted_mva200	200 Day MA
1780	ASDAX.Open_SmoothDer	Derivative of Smoothed
1789	ASDAX.High_SmoothDer	Derivative of Smoothed
1798	ASDAX.Low_SmoothDer	Derivative of Smoothed
1807	ASDAX.Close_SmoothDer	Derivative of Smoothed
1811	ASDAX.Volume_YoY	Year over Year
1812	ASDAX.Volume_YoY4	4 Year over 4 Year
1813	ASDAX.Volume_YoY5	5 Year over 5 Year
1814	ASDAX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1815	ASDAX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1816	ASDAX.Volume_SmoothDer	Derivative of Smoothed
1817	ASDAX.Volume_Log	Log of
1818	ASDAX.Volume_mva200	200 Day MA
1819	ASDAX.Volume_mva050	50 Day MA
1825	ASDAX.Adjusted_SmoothDer	Derivative of Smoothed
1827	ASDAX.Adjusted_mva200	200 Day MA
1834	TRBUX.Open_SmoothDer	Derivative of Smoothed
1843	TRBUX.High_SmoothDer	Derivative of Smoothed
1852	TRBUX.Low_SmoothDer	Derivative of Smoothed
1861	TRBUX.Close_SmoothDer	Derivative of Smoothed
1865	TRBUX.Volume_YoY	Year over Year
1866	TRBUX.Volume_YoY4	4 Year over 4 Year
1867	TRBUX.Volume_YoY5	5 Year over 5 Year
1868	TRBUX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1869	TRBUX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1870	TRBUX.Volume_SmoothDer	Derivative of Smoothed
1871	TRBUX.Volume_Log	Log of
1872	TRBUX.Volume_mva200	200 Day MA
1873	TRBUX.Volume_mva050	50 Day MA
1879	TRBUX.Adjusted_SmoothDer	Derivative of Smoothed
1886	PRWCX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1888	PRWCX.Open_SmoothDer	Derivative of Smoothed
1890	PRWCX.Open_mva200	200 Day MA
1891	PRWCX.Open_mva050	50 Day MA
1895	PRWCX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1897	PRWCX.High_SmoothDer	Derivative of Smoothed
1899	PRWCX.High_mva200	200 Day MA
1900	PRWCX.High_mva050	50 Day MA
1904	PRWCX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1906	PRWCX.Low_SmoothDer	Derivative of Smoothed
1908	PRWCX.Low_mva200	200 Day MA
1909	PRWCX.Low_mva050	50 Day MA
1913	PRWCX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1915	PRWCX.Close_SmoothDer	Derivative of Smoothed
1917	PRWCX.Close_mva200	200 Day MA
1918	PRWCX.Close_mva050	50 Day MA
1919	PRWCX.Volume_YoY	Year over Year
1920	PRWCX.Volume_YoY4	4 Year over 4 Year
1921	PRWCX.Volume_YoY5	5 Year over 5 Year
1922	PRWCX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1923	PRWCX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1924	PRWCX.Volume_SmoothDer	Derivative of Smoothed
1925	PRWCX.Volume_Log	Log of
1926	PRWCX.Volume_mva200	200 Day MA
1927	PRWCX.Volume_mva050	50 Day MA
1931	PRWCX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1933	PRWCX.Adjusted_SmoothDer	Derivative of Smoothed
1935	PRWCX.Adjusted_mva200	200 Day MA
1936	PRWCX.Adjusted_mva050	50 Day MA
1940	ADOZX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1943	ADOZX.Open_Log	Log of
1944	ADOZX.Open_mva200	200 Day MA
1945	ADOZX.Open_mva050	50 Day MA
1949	ADOZX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1952	ADOZX.High_Log	Log of
1953	ADOZX.High_mva200	200 Day MA
1954	ADOZX.High_mva050	50 Day MA
1958	ADOZX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1961	ADOZX.Low_Log	Log of
1962	ADOZX.Low_mva200	200 Day MA
1963	ADOZX.Low_mva050	50 Day MA
1967	ADOZX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1970	ADOZX.Close_Log	Log of
1971	ADOZX.Close_mva200	200 Day MA
1972	ADOZX.Close_mva050	50 Day MA
1973	ADOZX.Volume_YoY	Year over Year
1974	ADOZX.Volume_YoY4	4 Year over 4 Year
1975	ADOZX.Volume_YoY5	5 Year over 5 Year
1976	ADOZX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1977	ADOZX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
1978	ADOZX.Volume_SmoothDer	Derivative of Smoothed
1979	ADOZX.Volume_Log	Log of
1980	ADOZX.Volume_mva200	200 Day MA
1981	ADOZX.Volume_mva050	50 Day MA
1985	ADOZX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
1988	ADOZX.Adjusted_Log	Log of
1989	ADOZX.Adjusted_mva200	200 Day MA
1990	ADOZX.Adjusted_mva050	50 Day MA
2027	MERFX.Volume_YoY	Year over Year
2028	MERFX.Volume_YoY4	4 Year over 4 Year
2029	MERFX.Volume_YoY5	5 Year over 5 Year
2030	MERFX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2031	MERFX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2032	MERFX.Volume_SmoothDer	Derivative of Smoothed
2033	MERFX.Volume_Log	Log of
2034	MERFX.Volume_mva200	200 Day MA
2035	MERFX.Volume_mva050	50 Day MA
2048	CMNIX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2052	CMNIX.Open_mva200	200 Day MA
2053	CMNIX.Open_mva050	50 Day MA
2057	CMNIX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2061	CMNIX.High_mva200	200 Day MA
2062	CMNIX.High_mva050	50 Day MA
2066	CMNIX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2070	CMNIX.Low_mva200	200 Day MA
2071	CMNIX.Low_mva050	50 Day MA
2075	CMNIX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2079	CMNIX.Close_mva200	200 Day MA
2080	CMNIX.Close_mva050	50 Day MA
2081	CMNIX.Volume_YoY	Year over Year
2082	CMNIX.Volume_YoY4	4 Year over 4 Year
2083	CMNIX.Volume_YoY5	5 Year over 5 Year
2084	CMNIX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2085	CMNIX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2086	CMNIX.Volume_SmoothDer	Derivative of Smoothed
2087	CMNIX.Volume_Log	Log of
2088	CMNIX.Volume_mva200	200 Day MA
2089	CMNIX.Volume_mva050	50 Day MA
2093	CMNIX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2095	CMNIX.Adjusted_SmoothDer	Derivative of Smoothed
2097	CMNIX.Adjusted_mva200	200 Day MA
2098	CMNIX.Adjusted_mva050	50 Day MA
2102	CIHEX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2104	CIHEX.Open_SmoothDer	Derivative of Smoothed
2106	CIHEX.Open_mva200	200 Day MA
2107	CIHEX.Open_mva050	50 Day MA
2111	CIHEX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2113	CIHEX.High_SmoothDer	Derivative of Smoothed
2115	CIHEX.High_mva200	200 Day MA
2116	CIHEX.High_mva050	50 Day MA
2120	CIHEX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2122	CIHEX.Low_SmoothDer	Derivative of Smoothed
2124	CIHEX.Low_mva200	200 Day MA
2125	CIHEX.Low_mva050	50 Day MA
2129	CIHEX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2131	CIHEX.Close_SmoothDer	Derivative of Smoothed
2133	CIHEX.Close_mva200	200 Day MA
2134	CIHEX.Close_mva050	50 Day MA
2135	CIHEX.Volume_YoY	Year over Year
2136	CIHEX.Volume_YoY4	4 Year over 4 Year
2137	CIHEX.Volume_YoY5	5 Year over 5 Year
2138	CIHEX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2139	CIHEX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2140	CIHEX.Volume_SmoothDer	Derivative of Smoothed
2141	CIHEX.Volume_Log	Log of
2142	CIHEX.Volume_mva200	200 Day MA
2143	CIHEX.Volume_mva050	50 Day MA
2147	CIHEX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2151	CIHEX.Adjusted_mva200	200 Day MA
2152	CIHEX.Adjusted_mva050	50 Day MA
2156	IMPCH_Smooth	Savitsky-Golay Smoothed (p=3, n=365) U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2158	IMPCH_SmoothDer	Derivative of Smoothed U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2159	IMPCH_Log	Log of U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2161	IMPCH_mva050	U.S. Imports of Goods by Customs Basis from China (Monthly, NSA) 50 Day MA
2167	EXPCH_SmoothDer	Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
2176	IMPMX_SmoothDer	Derivative of Smoothed U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2178	IMPMX_mva200	U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) 200 Day MA
2185	EXPMX_SmoothDer	Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
2187	EXPMX_mva200	U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA) 200 Day MA
2194	HSN1FNSA_SmoothDer	Derivative of Smoothed New One Family Houses Sold: United States (Monthly, NSA)
2201	HNFSUSNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) New One Family Houses for Sale in the United States (Monthly, NSA)
2204	HNFSUSNSA_Log	Log of New One Family Houses for Sale in the United States (Monthly, NSA)
2205	HNFSUSNSA_mva200	New One Family Houses for Sale in the United States (Monthly, NSA) 200 Day MA
2206	HNFSUSNSA_mva050	New One Family Houses for Sale in the United States (Monthly, NSA) 50 Day MA
2234	REALLNNSA_YoY	Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2237	REALLNNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Monthly, NSA)
2239	REALLNNSA_SmoothDer	Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, NSA)
2240	REALLNNSA_Log	Log of Real Estate Loans, All Commercial Banks (Monthly, NSA)
2242	REALLNNSA_mva050	Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2243	REALLN_YoY	Real Estate Loans, All Commercial Banks (Monthly, SA) Year over Year
2246	REALLN_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Monthly, SA)
2248	REALLN_SmoothDer	Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, SA)
2249	REALLN_Log	Log of Real Estate Loans, All Commercial Banks (Monthly, SA)
2251	REALLN_mva050	Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2255	RELACBW027NBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, NSA)
2257	RELACBW027NBOG_SmoothDer	Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, NSA)
2260	RELACBW027NBOG_mva050	Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2261	RELACBW027SBOG_YoY	Real Estate Loans, All Commercial Banks (Weekly, SA) Year over Year
2264	RELACBW027SBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, SA)
2266	RELACBW027SBOG_SmoothDer	Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, SA)
2267	RELACBW027SBOG_Log	Log of Real Estate Loans, All Commercial Banks (Weekly, SA)
2269	RELACBW027SBOG_mva050	Real Estate Loans, All Commercial Banks (Weekly, SA) 50 Day MA
2270	RREACBM027NBOG_YoY	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2273	RREACBM027NBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2275	RREACBM027NBOG_SmoothDer	Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2276	RREACBM027NBOG_Log	Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2279	RREACBM027SBOG_YoY	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) Year over Year
2284	RREACBM027SBOG_SmoothDer	Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2288	RREACBW027SBOG_YoY	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) Year over Year
2291	RREACBW027SBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2293	RREACBW027SBOG_SmoothDer	Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2294	RREACBW027SBOG_Log	Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2300	RREACBW027NBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2302	RREACBW027NBOG_SmoothDer	Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2305	RREACBW027NBOG_mva050	Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2306	MORTGAGE30US_YoY	30-Year Fixed Rate Mortgage Average in the United States Year over Year
2313	MORTGAGE30US_mva200	30-Year Fixed Rate Mortgage Average in the United States 200 Day MA
2318	CONSUMERNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans, All Commercial Banks
2320	CONSUMERNSA_SmoothDer	Derivative of Smoothed Consumer Loans, All Commercial Banks
2321	CONSUMERNSA_Log	Log of Consumer Loans, All Commercial Banks
2322	CONSUMERNSA_mva200	Consumer Loans, All Commercial Banks 200 Day MA
2323	CONSUMERNSA_mva050	Consumer Loans, All Commercial Banks 50 Day MA
2327	TOTLLNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Loans and Leases in Bank Credit, All Commercial Banks
2332	TOTLLNSA_mva050	Loans and Leases in Bank Credit, All Commercial Banks 50 Day MA
2336	DPSACBW027SBOG_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Deposits, All Commercial Banks
2339	DPSACBW027SBOG_Log	Log of Deposits, All Commercial Banks
2340	DPSACBW027SBOG_mva200	Deposits, All Commercial Banks 200 Day MA
2341	DPSACBW027SBOG_mva050	Deposits, All Commercial Banks 50 Day MA
2347	DRCLACBS_SmoothDer	Derivative of Smoothed Delinquency Rate on Consumer Loans, All Commercial Banks, SA
2361	SRPSABSNNCB_YoY4	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 4 Year over 4 Year
2365	SRPSABSNNCB_SmoothDer	Derivative of Smoothed Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2366	SRPSABSNNCB_Log	Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2368	SRPSABSNNCB_mva050	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 50 Day MA
2372	ASTLL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; total loans; liability, Level (NSA)
2375	ASTLL_Log	Log of All sectors; total loans; liability, Level (NSA)
2376	ASTLL_mva200	All sectors; total loans; liability, Level (NSA) 200 Day MA
2377	ASTLL_mva050	All sectors; total loans; liability, Level (NSA) 50 Day MA
2378	FBDILNECA_YoY	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) Year over Year
2384	FBDILNECA_Log	Log of Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2385	FBDILNECA_mva200	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 200 Day MA
2386	FBDILNECA_mva050	Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 50 Day MA
2389	ASOLAL_YoY5	All sectors; other loans and advances; liability, Level (NSA) 5 Year over 5 Year
2390	ASOLAL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; other loans and advances; liability, Level (NSA)
2393	ASOLAL_Log	Log of All sectors; other loans and advances; liability, Level (NSA)
2394	ASOLAL_mva200	All sectors; other loans and advances; liability, Level (NSA) 200 Day MA
2395	ASOLAL_mva050	All sectors; other loans and advances; liability, Level (NSA) 50 Day MA
2399	ASTMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; total mortgages; asset, Level (NSA)
2402	ASTMA_Log	Log of All sectors; total mortgages; asset, Level (NSA)
2403	ASTMA_mva200	All sectors; total mortgages; asset, Level (NSA) 200 Day MA
2404	ASTMA_mva050	All sectors; total mortgages; asset, Level (NSA) 50 Day MA
2408	ASHMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; home mortgages; asset, Level (NSA)
2411	ASHMA_Log	Log of All sectors; home mortgages; asset, Level (NSA)
2412	ASHMA_mva200	All sectors; home mortgages; asset, Level (NSA) 200 Day MA
2413	ASHMA_mva050	All sectors; home mortgages; asset, Level (NSA) 50 Day MA
2417	ASMRMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; multifamily residential mortgages; asset, Level (NSA)
2420	ASMRMA_Log	Log of All sectors; multifamily residential mortgages; asset, Level (NSA)
2421	ASMRMA_mva200	All sectors; multifamily residential mortgages; asset, Level (NSA) 200 Day MA
2422	ASMRMA_mva050	All sectors; multifamily residential mortgages; asset, Level (NSA) 50 Day MA
2426	ASCMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; commercial mortgages; asset, Level (NSA)
2429	ASCMA_Log	Log of All sectors; commercial mortgages; asset, Level (NSA)
2430	ASCMA_mva200	All sectors; commercial mortgages; asset, Level (NSA) 200 Day MA
2431	ASCMA_mva050	All sectors; commercial mortgages; asset, Level (NSA) 50 Day MA
2435	ASFMA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All sectors; farm mortgages; asset, Level (NSA)
2438	ASFMA_Log	Log of All sectors; farm mortgages; asset, Level (NSA)
2439	ASFMA_mva200	All sectors; farm mortgages; asset, Level (NSA) 200 Day MA
2440	ASFMA_mva050	All sectors; farm mortgages; asset, Level (NSA) 50 Day MA
2446	CCLBSHNO_SmoothDer	Derivative of Smoothed Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2447	CCLBSHNO_Log	Log of Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2449	CCLBSHNO_mva050	Households and nonprofit organizations; consumer credit; liability, Level (NSA) 50 Day MA
2453	FBDSILQ027S_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors debt securities; liability, Level (NSA)
2456	FBDSILQ027S_Log	Log of Domestic financial sectors debt securities; liability, Level (NSA)
2457	FBDSILQ027S_mva200	Domestic financial sectors debt securities; liability, Level (NSA) 200 Day MA
2458	FBDSILQ027S_mva050	Domestic financial sectors debt securities; liability, Level (NSA) 50 Day MA
2460	FBLL_YoY4	Domestic financial sectors loans; liability, Level (NSA) 4 Year over 4 Year
2461	FBLL_YoY5	Domestic financial sectors loans; liability, Level (NSA) 5 Year over 5 Year
2462	FBLL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors loans; liability, Level (NSA)
2465	FBLL_Log	Log of Domestic financial sectors loans; liability, Level (NSA)
2467	FBLL_mva050	Domestic financial sectors loans; liability, Level (NSA) 50 Day MA
2474	NCBDBIQ027S_Log	Log of Nonfinancial corporate business; debt securities; liability, Level
2475	NCBDBIQ027S_mva200	Nonfinancial corporate business; debt securities; liability, Level 200 Day MA
2476	NCBDBIQ027S_mva050	Nonfinancial corporate business; debt securities; liability, Level 50 Day MA
2522	TNX.Volume_YoY	Year over Year
2523	TNX.Volume_YoY4	4 Year over 4 Year
2524	TNX.Volume_YoY5	5 Year over 5 Year
2525	TNX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2526	TNX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2527	TNX.Volume_SmoothDer	Derivative of Smoothed
2528	TNX.Volume_Log	Log of
2529	TNX.Volume_mva200	200 Day MA
2530	TNX.Volume_mva050	50 Day MA
2546	CLF.Open_Log	Log of
2547	CLF.Open_mva200	200 Day MA
2556	CLF.High_mva200	200 Day MA
2564	CLF.Low_Log	Log of
2565	CLF.Low_mva200	200 Day MA
2573	CLF.Close_Log	Log of
2574	CLF.Close_mva200	200 Day MA
2582	CLF.Volume_Log	Log of
2591	CLF.Adjusted_Log	Log of
2592	CLF.Adjusted_mva200	200 Day MA
2606	DGS1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 1-Year Treasury Constant Maturity Rate
2608	DGS1_SmoothDer	Derivative of Smoothed 1-Year Treasury Constant Maturity Rate
2615	DGS2_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 2-Year Treasury Constant Maturity Rate
2617	DGS2_SmoothDer	Derivative of Smoothed 2-Year Treasury Constant Maturity Rate
2619	DGS2_mva200	2-Year Treasury Constant Maturity Rate 200 Day MA
2624	TB3MS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2626	TB3MS_SmoothDer	Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2627	TB3MS_Log	Log of 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2629	TB3MS_mva050	3-Month Treasury Bill: Secondary Market Rate (Monthly) 50 Day MA
2633	DTB3_Smooth	Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Daily)
2635	DTB3_SmoothDer	Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Daily)
2636	DTB3_Log	Log of 3-Month Treasury Bill: Secondary Market Rate (Daily)
2645	IRX.Open_Log	Log of
2654	IRX.High_Log	Log of
2660	IRX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2662	IRX.Low_SmoothDer	Derivative of Smoothed
2663	IRX.Low_Log	Log of
2669	IRX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2671	IRX.Close_SmoothDer	Derivative of Smoothed
2672	IRX.Close_Log	Log of
2675	IRX.Volume_YoY	Year over Year
2676	IRX.Volume_YoY4	4 Year over 4 Year
2677	IRX.Volume_YoY5	5 Year over 5 Year
2678	IRX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2679	IRX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2680	IRX.Volume_SmoothDer	Derivative of Smoothed
2681	IRX.Volume_Log	Log of
2682	IRX.Volume_mva200	200 Day MA
2683	IRX.Volume_mva050	50 Day MA
2687	IRX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2689	IRX.Adjusted_SmoothDer	Derivative of Smoothed
2690	IRX.Adjusted_Log	Log of
2699	DCOILWTICO_Log	Log of Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma
2700	DCOILWTICO_mva200	Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma 200 Day MA
2709	DCOILBRENTEU_mva200	Crude Oil Prices: Brent - Europe 200 Day MA
2717	NEWORDER_Log	Log of Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
2718	NEWORDER_mva200	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 200 Day MA
2719	NEWORDER_mva050	Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 50 Day MA
2741	GSPC.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2743	GSPC.Open_SmoothDer	Derivative of Smoothed
2745	GSPC.Open_mva200	200 Day MA
2746	GSPC.Open_mva050	50 Day MA
2750	GSPC.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2752	GSPC.High_SmoothDer	Derivative of Smoothed
2754	GSPC.High_mva200	200 Day MA
2755	GSPC.High_mva050	50 Day MA
2759	GSPC.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2761	GSPC.Low_SmoothDer	Derivative of Smoothed
2763	GSPC.Low_mva200	200 Day MA
2764	GSPC.Low_mva050	50 Day MA
2768	GSPC.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2770	GSPC.Close_SmoothDer	Derivative of Smoothed
2772	GSPC.Close_mva200	200 Day MA
2773	GSPC.Close_mva050	50 Day MA
2786	GSPC.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2788	GSPC.Adjusted_SmoothDer	Derivative of Smoothed
2790	GSPC.Adjusted_mva200	200 Day MA
2791	GSPC.Adjusted_mva050	50 Day MA
2794	RLG.Open_YoY5	5 Year over 5 Year
2795	RLG.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2797	RLG.Open_SmoothDer	Derivative of Smoothed
2799	RLG.Open_mva200	200 Day MA
2800	RLG.Open_mva050	50 Day MA
2803	RLG.High_YoY5	5 Year over 5 Year
2804	RLG.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2806	RLG.High_SmoothDer	Derivative of Smoothed
2808	RLG.High_mva200	200 Day MA
2809	RLG.High_mva050	50 Day MA
2812	RLG.Low_YoY5	5 Year over 5 Year
2813	RLG.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2815	RLG.Low_SmoothDer	Derivative of Smoothed
2817	RLG.Low_mva200	200 Day MA
2818	RLG.Low_mva050	50 Day MA
2822	RLG.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2824	RLG.Close_SmoothDer	Derivative of Smoothed
2826	RLG.Close_mva200	200 Day MA
2827	RLG.Close_mva050	50 Day MA
2828	RLG.Volume_YoY	Year over Year
2829	RLG.Volume_YoY4	4 Year over 4 Year
2830	RLG.Volume_YoY5	5 Year over 5 Year
2831	RLG.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2832	RLG.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
2833	RLG.Volume_SmoothDer	Derivative of Smoothed
2834	RLG.Volume_Log	Log of
2835	RLG.Volume_mva200	200 Day MA
2836	RLG.Volume_mva050	50 Day MA
2840	RLG.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
2842	RLG.Adjusted_SmoothDer	Derivative of Smoothed
2844	RLG.Adjusted_mva200	200 Day MA
2845	RLG.Adjusted_mva050	50 Day MA
2853	DJI.Open_mva200	200 Day MA
2854	DJI.Open_mva050	50 Day MA
2862	DJI.High_mva200	200 Day MA
2863	DJI.High_mva050	50 Day MA
2871	DJI.Low_mva200	200 Day MA
2872	DJI.Low_mva050	50 Day MA
2880	DJI.Close_mva200	200 Day MA
2881	DJI.Close_mva050	50 Day MA
2898	DJI.Adjusted_mva200	200 Day MA
2899	DJI.Adjusted_mva050	50 Day MA
2907	STOXX50E.Open_mva200	200 Day MA
2908	STOXX50E.Open_mva050	50 Day MA
2916	STOXX50E.High_mva200	200 Day MA
2917	STOXX50E.High_mva050	50 Day MA
2925	STOXX50E.Low_mva200	200 Day MA
2926	STOXX50E.Low_mva050	50 Day MA
2934	STOXX50E.Close_mva200	200 Day MA
2935	STOXX50E.Close_mva050	50 Day MA
2942	STOXX50E.Volume_Log	Log of
2952	STOXX50E.Adjusted_mva200	200 Day MA
2953	STOXX50E.Adjusted_mva050	50 Day MA
2961	EFA.Open_mva200	200 Day MA
2962	EFA.Open_mva050	50 Day MA
2970	EFA.High_mva200	200 Day MA
2971	EFA.High_mva050	50 Day MA
2979	EFA.Low_mva200	200 Day MA
2980	EFA.Low_mva050	50 Day MA
2988	EFA.Close_mva200	200 Day MA
2989	EFA.Close_mva050	50 Day MA
3002	EFA.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3006	EFA.Adjusted_mva200	200 Day MA
3007	EFA.Adjusted_mva050	50 Day MA
3014	GDP_Log	Log of Gross Domestic Product
3015	GDP_mva200	Gross Domestic Product 200 Day MA
3016	GDP_mva050	Gross Domestic Product 50 Day MA
3017	FNDEFX_YoY	Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate) Year over Year
3031	FDEFX_SmoothDer	Derivative of Smoothed Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3032	FDEFX_Log	Log of Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3033	FDEFX_mva200	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 200 Day MA
3034	FDEFX_mva050	Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 50 Day MA
3040	GDPNOW_SmoothDer	Derivative of Smoothed Fed Atlanta GDPNow
3041	GDPNOW_Log	Log of Fed Atlanta GDPNow
3050	GDPC1_Log	Log of Real Gross Domestic Product
3051	GDPC1_mva200	Real Gross Domestic Product 200 Day MA
3052	GDPC1_mva050	Real Gross Domestic Product 50 Day MA
3059	GDPDEF_Log	Log of Gross Domestic Product: Implicit Price Deflator
3060	GDPDEF_mva200	Gross Domestic Product: Implicit Price Deflator 200 Day MA
3061	GDPDEF_mva050	Gross Domestic Product: Implicit Price Deflator 50 Day MA
3065	VIG.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3067	VIG.Open_SmoothDer	Derivative of Smoothed
3069	VIG.Open_mva200	200 Day MA
3070	VIG.Open_mva050	50 Day MA
3074	VIG.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3076	VIG.High_SmoothDer	Derivative of Smoothed
3078	VIG.High_mva200	200 Day MA
3079	VIG.High_mva050	50 Day MA
3083	VIG.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3085	VIG.Low_SmoothDer	Derivative of Smoothed
3087	VIG.Low_mva200	200 Day MA
3088	VIG.Low_mva050	50 Day MA
3092	VIG.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3094	VIG.Close_SmoothDer	Derivative of Smoothed
3096	VIG.Close_mva200	200 Day MA
3097	VIG.Close_mva050	50 Day MA
3110	VIG.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3112	VIG.Adjusted_SmoothDer	Derivative of Smoothed
3114	VIG.Adjusted_mva200	200 Day MA
3115	VIG.Adjusted_mva050	50 Day MA
3116	WLRRAL_YoY	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Year over Year
3118	WLRRAL_YoY5	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 5 Year over 5 Year
3119	WLRRAL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3120	WLRRAL_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3121	WLRRAL_SmoothDer	Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3122	WLRRAL_Log	Log of Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3123	WLRRAL_mva200	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 200 Day MA
3124	WLRRAL_mva050	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 50 Day MA
3128	FEDFUNDS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Effective Federal Funds Rate
3130	FEDFUNDS_SmoothDer	Derivative of Smoothed Effective Federal Funds Rate
3137	GPDI_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Gross Private Domestic Investment
3139	GPDI_SmoothDer	Derivative of Smoothed Gross Private Domestic Investment
3148	W790RC1Q027SBEA_SmoothDer	Derivative of Smoothed Net domestic investment: Private: Domestic busines
3149	W790RC1Q027SBEA_Log	Log of Net domestic investment: Private: Domestic busines
3157	MZMV_SmoothDer	Derivative of Smoothed Velocity of MZM Money Stock
3158	MZMV_Log	Log of Velocity of MZM Money Stock
3160	MZMV_mva050	Velocity of MZM Money Stock 50 Day MA
3166	M1_SmoothDer	Derivative of Smoothed M1 Money Stock
3167	M1_Log	Log of M1 Money Stock
3176	M2_Log	Log of M2 Money Stock
3185	OPHNFB_Log	Log of Nonfarm Business Sector: Real Output Per Hour of All Persons
3186	OPHNFB_mva200	Nonfarm Business Sector: Real Output Per Hour of All Persons 200 Day MA
3187	OPHNFB_mva050	Nonfarm Business Sector: Real Output Per Hour of All Persons 50 Day MA
3191	IPMAN_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Industrial Production: Manufacturing (NAICS)
3193	IPMAN_SmoothDer	Derivative of Smoothed Industrial Production: Manufacturing (NAICS)
3194	IPMAN_Log	Log of Industrial Production: Manufacturing (NAICS)
3195	IPMAN_mva200	Industrial Production: Manufacturing (NAICS) 200 Day MA
3196	IPMAN_mva050	Industrial Production: Manufacturing (NAICS) 50 Day MA
3204	IWD.Open_mva200	200 Day MA
3205	IWD.Open_mva050	50 Day MA
3213	IWD.High_mva200	200 Day MA
3214	IWD.High_mva050	50 Day MA
3222	IWD.Low_mva200	200 Day MA
3223	IWD.Low_mva050	50 Day MA
3231	IWD.Close_mva200	200 Day MA
3232	IWD.Close_mva050	50 Day MA
3249	IWD.Adjusted_mva200	200 Day MA
3250	IWD.Adjusted_mva050	50 Day MA
3258	GS5_mva200	5-Year Treasury Constant Maturity Rate 200 Day MA
3274	VIXCLS_SmoothDer	Derivative of Smoothed CBOE Volatility Index
3283	VXX.Open_SmoothDer	Derivative of Smoothed
3292	VXX.High_SmoothDer	Derivative of Smoothed
3301	VXX.Low_SmoothDer	Derivative of Smoothed
3310	VXX.Close_SmoothDer	Derivative of Smoothed
3315	VXX.Volume_YoY4	4 Year over 4 Year
3316	VXX.Volume_YoY5	5 Year over 5 Year
3320	VXX.Volume_Log	Log of
3321	VXX.Volume_mva200	200 Day MA
3328	VXX.Adjusted_SmoothDer	Derivative of Smoothed
3337	HOUST1F_SmoothDer	Derivative of Smoothed Privately Owned Housing Starts: 1-Unit Structures
3347	GFDEBTN_Log	Log of Federal Debt: Total Public Debt
3348	GFDEBTN_mva200	Federal Debt: Total Public Debt 200 Day MA
3349	GFDEBTN_mva050	Federal Debt: Total Public Debt 50 Day MA
3355	HOUST_SmoothDer	Derivative of Smoothed Housing Starts: Total: New Privately Owned Housing Units Started
3365	MSPUS_Log	Log of Median Sales Price of Houses Sold for the United States
3366	MSPUS_mva200	Median Sales Price of Houses Sold for the United States 200 Day MA
3367	MSPUS_mva050	Median Sales Price of Houses Sold for the United States 50 Day MA
3382	DGORDER_SmoothDer	Derivative of Smoothed Manufacturers’ New Orders: Durable Goods (SA)
3384	DGORDER_mva200	Manufacturers’ New Orders: Durable Goods (SA) 200 Day MA
3392	CSUSHPINSA_Log	Log of S&P/Case-Shiller U.S. National Home Price Index (NSA)
3393	CSUSHPINSA_mva200	S&P/Case-Shiller U.S. National Home Price Index (NSA) 200 Day MA
3394	CSUSHPINSA_mva050	S&P/Case-Shiller U.S. National Home Price Index (NSA) 50 Day MA
3400	GFDEGDQ188S_SmoothDer	Derivative of Smoothed Federal Debt: Total Public Debt as Percent of Gross Domestic Product
3408	FYFSD_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit
3410	FYFSD_Log	Log of Federal Surplus or Deficit
3412	FYFSD_mva050	Federal Surplus or Deficit 50 Day MA
3413	FYFSGDA188S_YoY	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product Year over Year
3417	FYFSGDA188S_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
3419	FYFSGDA188S_Log	Log of Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
3420	FYFSGDA188S_mva200	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 200 Day MA
3421	FYFSGDA188S_mva050	Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 50 Day MA
3425	GOLDAMGBD228NLBM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Gold Fixing Price 10:30 A.M. (London time) in London Bullion Market
3427	GOLDAMGBD228NLBM_SmoothDer	Derivative of Smoothed Gold Fixing Price 10:30 A.M. (London time) in London Bullion Market
3436	GDX.Open_SmoothDer	Derivative of Smoothed
3445	GDX.High_SmoothDer	Derivative of Smoothed
3454	GDX.Low_SmoothDer	Derivative of Smoothed
3463	GDX.Close_SmoothDer	Derivative of Smoothed
3470	GDX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3472	GDX.Volume_SmoothDer	Derivative of Smoothed
3481	GDX.Adjusted_SmoothDer	Derivative of Smoothed
3492	XLE.Open_mva200	200 Day MA
3501	XLE.High_mva200	200 Day MA
3510	XLE.Low_mva200	200 Day MA
3519	XLE.Close_mva200	200 Day MA
3537	XLE.Adjusted_mva200	200 Day MA
3546	GSG.Open_mva200	200 Day MA
3555	GSG.High_mva200	200 Day MA
3564	GSG.Low_mva200	200 Day MA
3573	GSG.Close_mva200	200 Day MA
3591	GSG.Adjusted_mva200	200 Day MA
3596	WALCL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets
3597	WALCL_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) All Federal Reserve Banks: Total Assets
3598	WALCL_SmoothDer	Derivative of Smoothed All Federal Reserve Banks: Total Assets
3599	WALCL_Log	Log of All Federal Reserve Banks: Total Assets
3600	WALCL_mva200	All Federal Reserve Banks: Total Assets 200 Day MA
3601	WALCL_mva050	All Federal Reserve Banks: Total Assets 50 Day MA
3604	OUTMS_YoY5	Manufacturing Sector: Real Output 5 Year over 5 Year
3607	OUTMS_SmoothDer	Derivative of Smoothed Manufacturing Sector: Real Output
3608	OUTMS_Log	Log of Manufacturing Sector: Real Output
3609	OUTMS_mva200	Manufacturing Sector: Real Output 200 Day MA
3610	OUTMS_mva050	Manufacturing Sector: Real Output 50 Day MA
3613	MANEMP_YoY5	All Employees: Manufacturing 5 Year over 5 Year
3614	MANEMP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All Employees: Manufacturing
3616	MANEMP_SmoothDer	Derivative of Smoothed All Employees: Manufacturing
3617	MANEMP_Log	Log of All Employees: Manufacturing
3618	MANEMP_mva200	All Employees: Manufacturing 200 Day MA
3619	MANEMP_mva050	All Employees: Manufacturing 50 Day MA
3622	PRS30006163_YoY5	Manufacturing Sector: Real Output Per Person 5 Year over 5 Year
3625	PRS30006163_SmoothDer	Derivative of Smoothed Manufacturing Sector: Real Output Per Person
3626	PRS30006163_Log	Log of Manufacturing Sector: Real Output Per Person
3627	PRS30006163_mva200	Manufacturing Sector: Real Output Per Person 200 Day MA
3628	PRS30006163_mva050	Manufacturing Sector: Real Output Per Person 50 Day MA
3648	SOFR_YoY4	Secured Overnight Financing Rate 4 Year over 4 Year
3649	SOFR_YoY5	Secured Overnight Financing Rate 5 Year over 5 Year
3650	SOFR_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate
3652	SOFR_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate
3653	SOFR_Log	Log of Secured Overnight Financing Rate
3655	SOFR_mva050	Secured Overnight Financing Rate 50 Day MA
3659	SOFRVOL_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Volume
3666	SOFR99_YoY4	Secured Overnight Financing Rate: 99th Percentile 4 Year over 4 Year
3667	SOFR99_YoY5	Secured Overnight Financing Rate: 99th Percentile 5 Year over 5 Year
3670	SOFR99_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate: 99th Percentile
3671	SOFR99_Log	Log of Secured Overnight Financing Rate: 99th Percentile
3673	SOFR99_mva050	Secured Overnight Financing Rate: 99th Percentile 50 Day MA
3677	SOFR75_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 75th Percentile
3679	SOFR75_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate: 75th Percentile
3684	SOFR25_YoY4	Secured Overnight Financing Rate: 25th Percentile 4 Year over 4 Year
3685	SOFR25_YoY5	Secured Overnight Financing Rate: 25th Percentile 5 Year over 5 Year
3686	SOFR25_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 25th Percentile
3688	SOFR25_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate: 25th Percentile
3689	SOFR25_Log	Log of Secured Overnight Financing Rate: 25th Percentile
3691	SOFR25_mva050	Secured Overnight Financing Rate: 25th Percentile 50 Day MA
3695	SOFR1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 1st Percentile
3697	SOFR1_SmoothDer	Derivative of Smoothed Secured Overnight Financing Rate: 1st Percentile
3698	SOFR1_Log	Log of Secured Overnight Financing Rate: 1st Percentile
3704	OBFR_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate
3706	OBFR_SmoothDer	Derivative of Smoothed Overnight Bank Funding Rate
3713	OBFR99_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 99th Percentile
3715	OBFR99_SmoothDer	Derivative of Smoothed Overnight Bank Funding Rate: 99th Percentile
3722	OBFR75_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 75th Percentile
3724	OBFR75_SmoothDer	Derivative of Smoothed Overnight Bank Funding Rate: 75th Percentile
3731	OBFR25_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 25th Percentile
3733	OBFR25_SmoothDer	Derivative of Smoothed Overnight Bank Funding Rate: 25th Percentile
3740	OBFR1_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 1st Percentile
3742	OBFR1_SmoothDer	Derivative of Smoothed Overnight Bank Funding Rate: 1st Percentile
3746	RPONTSYD_YoY	Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations Year over Year
3752	RPONTSYD_Log	Log of Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
3755	IOER_YoY	Interest Rate on Excess Reserves Year over Year
3757	IOER_YoY5	Interest Rate on Excess Reserves 5 Year over 5 Year
3758	IOER_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Interest Rate on Excess Reserves
3760	IOER_SmoothDer	Derivative of Smoothed Interest Rate on Excess Reserves
3761	IOER_Log	Log of Interest Rate on Excess Reserves
3762	IOER_mva200	Interest Rate on Excess Reserves 200 Day MA
3763	IOER_mva050	Interest Rate on Excess Reserves 50 Day MA
3766	WRESBAL_YoY5	Reserve Balances with Federal Reserve Banks 5 Year over 5 Year
3768	WRESBAL_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Reserve Balances with Federal Reserve Banks
3770	WRESBAL_Log	Log of Reserve Balances with Federal Reserve Banks
3771	WRESBAL_mva200	Reserve Balances with Federal Reserve Banks 200 Day MA
3772	WRESBAL_mva050	Reserve Balances with Federal Reserve Banks 50 Day MA
3779	EXCSRESNW_Log	Log of Excess Reserves of Depository Institutions
3780	EXCSRESNW_mva200	Excess Reserves of Depository Institutions 200 Day MA
3781	EXCSRESNW_mva050	Excess Reserves of Depository Institutions 50 Day MA
3782	ECBASSETS_YoY	Central Bank Assets for Euro Area (11-19 Countries) Year over Year
3788	ECBASSETS_Log	Log of Central Bank Assets for Euro Area (11-19 Countries)
3789	ECBASSETS_mva200	Central Bank Assets for Euro Area (11-19 Countries) 200 Day MA
3790	ECBASSETS_mva050	Central Bank Assets for Euro Area (11-19 Countries) 50 Day MA
3796	EUNNGDP_SmoothDer	Derivative of Smoothed Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
3797	EUNNGDP_Log	Log of Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
3798	EUNNGDP_mva200	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 200 Day MA
3799	EUNNGDP_mva050	Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 50 Day MA
3803	CEU0600000007_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3805	CEU0600000007_SmoothDer	Derivative of Smoothed Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3806	CEU0600000007_Log	Log of Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3807	CEU0600000007_mva200	Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 200 Day MA
3808	CEU0600000007_mva050	Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 50 Day MA
3824	CURRENCY_Log	Log of Currency Component of M1 (Seasonally Adjusted)
3825	CURRENCY_mva200	Currency Component of M1 (Seasonally Adjusted) 200 Day MA
3826	CURRENCY_mva050	Currency Component of M1 (Seasonally Adjusted) 50 Day MA
3833	WCURRNS_Log	Log of Currency Component of M1
3834	WCURRNS_mva200	Currency Component of M1 200 Day MA
3835	WCURRNS_mva050	Currency Component of M1 50 Day MA
3838	BOGMBASE_YoY5	Monetary Base; Total 5 Year over 5 Year
3842	BOGMBASE_Log	Log of Monetary Base; Total
3843	BOGMBASE_mva200	Monetary Base; Total 200 Day MA
3844	BOGMBASE_mva050	Monetary Base; Total 50 Day MA
3847	PRS88003193_YoY5	Nonfinancial Corporations Sector: Unit Profits 5 Year over 5 Year
3851	PRS88003193_Log	Log of Nonfinancial Corporations Sector: Unit Profits
3852	PRS88003193_mva200	Nonfinancial Corporations Sector: Unit Profits 200 Day MA
3853	PRS88003193_mva050	Nonfinancial Corporations Sector: Unit Profits 50 Day MA
3860	PPIACO_Log	Log of Producer Price Index for All Commodities
3861	PPIACO_mva200	Producer Price Index for All Commodities 200 Day MA
3862	PPIACO_mva050	Producer Price Index for All Commodities 50 Day MA
3869	PCUOMFGOMFG_Log	Log of Producer Price Index by Industry: Total Manufacturing Industries
3870	PCUOMFGOMFG_mva200	Producer Price Index by Industry: Total Manufacturing Industries 200 Day MA
3871	PCUOMFGOMFG_mva050	Producer Price Index by Industry: Total Manufacturing Industries 50 Day MA
3878	POPTHM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Population (U.S.)
3879	POPTHM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Population (U.S.)
3884	POPTHM_Log	Log of Population (U.S.)
3885	POPTHM_Log	Log of Population (U.S.)
3886	POPTHM_mva200	Population (U.S.) 200 Day MA
3887	POPTHM_mva200	Population (U.S.) 200 Day MA
3888	POPTHM_mva050	Population (U.S.) 50 Day MA
3889	POPTHM_mva050	Population (U.S.) 50 Day MA
3896	POPTHM.1_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3897	POPTHM.1_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
3902	POPTHM.1_Log	Log of
3903	POPTHM.1_Log	Log of
3904	POPTHM.1_mva200	200 Day MA
3905	POPTHM.1_mva200	200 Day MA
3906	POPTHM.1_mva050	50 Day MA
3907	POPTHM.1_mva050	50 Day MA
3911	CLF16OV_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Civilian Labor Force Level, SA
3913	CLF16OV_SmoothDer	Derivative of Smoothed Civilian Labor Force Level, SA
3914	CLF16OV_Log	Log of Civilian Labor Force Level, SA
3915	CLF16OV_mva200	Civilian Labor Force Level, SA 200 Day MA
3916	CLF16OV_mva050	Civilian Labor Force Level, SA 50 Day MA
3922	LNU01000000_SmoothDer	Derivative of Smoothed Civilian Labor Force Level, NSA
3924	LNU01000000_mva200	Civilian Labor Force Level, NSA 200 Day MA
3926	LNU03000000_YoY	Unemployment Level (NSA) Year over Year
3951	RSAFS_mva200	Advance Retail Sales: Retail and Food Services 200 Day MA
3958	FRGSHPUSM649NCIS_SmoothDer	Derivative of Smoothed Cass Freight Index: Shipments
3965	BOPGTB_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Trade Balance: Goods, Balance of Payments Basis (SA)
3967	BOPGTB_SmoothDer	Derivative of Smoothed Trade Balance: Goods, Balance of Payments Basis (SA)
3968	BOPGTB_Log	Log of Trade Balance: Goods, Balance of Payments Basis (SA)
3970	BOPGTB_mva050	Trade Balance: Goods, Balance of Payments Basis (SA) 50 Day MA
3973	TERMCBPER24NS_YoY5	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 5 Year over 5 Year
3974	TERMCBPER24NS_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
3977	TERMCBPER24NS_Log	Log of Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
3978	TERMCBPER24NS_mva200	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 200 Day MA
3979	TERMCBPER24NS_mva050	Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 50 Day MA
3980	A065RC1A027NBEA_YoY	Personal income (NSA) Year over Year
3986	A065RC1A027NBEA_Log	Log of Personal income (NSA)
3987	A065RC1A027NBEA_mva200	Personal income (NSA) 200 Day MA
3988	A065RC1A027NBEA_mva050	Personal income (NSA) 50 Day MA
4004	PCE_Log	Log of Personal Consumption Expenditures (SA)
4005	PCE_mva200	Personal Consumption Expenditures (SA) 200 Day MA
4006	PCE_mva050	Personal Consumption Expenditures (SA) 50 Day MA
4008	A053RC1Q027SBEA_YoY4	National income: Corporate profits before tax (without IVA and CCAdj) 4 Year over 4 Year
4013	A053RC1Q027SBEA_Log	Log of National income: Corporate profits before tax (without IVA and CCAdj)
4014	A053RC1Q027SBEA_mva200	National income: Corporate profits before tax (without IVA and CCAdj) 200 Day MA
4015	A053RC1Q027SBEA_mva050	National income: Corporate profits before tax (without IVA and CCAdj) 50 Day MA
4022	CPROFIT_Log	Log of Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
4023	CPROFIT_mva200	Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 200 Day MA
4024	CPROFIT_mva050	Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 50 Day MA
4028	SPY.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4030	SPY.Open_SmoothDer	Derivative of Smoothed
4032	SPY.Open_mva200	200 Day MA
4033	SPY.Open_mva050	50 Day MA
4037	SPY.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4039	SPY.High_SmoothDer	Derivative of Smoothed
4041	SPY.High_mva200	200 Day MA
4042	SPY.High_mva050	50 Day MA
4046	SPY.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4048	SPY.Low_SmoothDer	Derivative of Smoothed
4050	SPY.Low_mva200	200 Day MA
4051	SPY.Low_mva050	50 Day MA
4055	SPY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4057	SPY.Close_SmoothDer	Derivative of Smoothed
4059	SPY.Close_mva200	200 Day MA
4060	SPY.Close_mva050	50 Day MA
4073	SPY.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4075	SPY.Adjusted_SmoothDer	Derivative of Smoothed
4077	SPY.Adjusted_mva200	200 Day MA
4078	SPY.Adjusted_mva050	50 Day MA
4086	MDY.Open_mva200	200 Day MA
4095	MDY.High_mva200	200 Day MA
4104	MDY.Low_mva200	200 Day MA
4113	MDY.Close_mva200	200 Day MA
4131	MDY.Adjusted_mva200	200 Day MA
4140	EES.Open_mva200	200 Day MA
4149	EES.High_mva200	200 Day MA
4158	EES.Low_mva200	200 Day MA
4167	EES.Close_mva200	200 Day MA
4175	EES.Volume_Log	Log of
4185	EES.Adjusted_mva200	200 Day MA
4194	IJR.Open_mva200	200 Day MA
4203	IJR.High_mva200	200 Day MA
4212	IJR.Low_mva200	200 Day MA
4221	IJR.Close_mva200	200 Day MA
4239	IJR.Adjusted_mva200	200 Day MA
4244	VGSTX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4246	VGSTX.Open_SmoothDer	Derivative of Smoothed
4248	VGSTX.Open_mva200	200 Day MA
4249	VGSTX.Open_mva050	50 Day MA
4253	VGSTX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4255	VGSTX.High_SmoothDer	Derivative of Smoothed
4257	VGSTX.High_mva200	200 Day MA
4258	VGSTX.High_mva050	50 Day MA
4262	VGSTX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4264	VGSTX.Low_SmoothDer	Derivative of Smoothed
4266	VGSTX.Low_mva200	200 Day MA
4267	VGSTX.Low_mva050	50 Day MA
4271	VGSTX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4273	VGSTX.Close_SmoothDer	Derivative of Smoothed
4275	VGSTX.Close_mva200	200 Day MA
4276	VGSTX.Close_mva050	50 Day MA
4277	VGSTX.Volume_YoY	Year over Year
4278	VGSTX.Volume_YoY4	4 Year over 4 Year
4279	VGSTX.Volume_YoY5	5 Year over 5 Year
4280	VGSTX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4281	VGSTX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
4282	VGSTX.Volume_SmoothDer	Derivative of Smoothed
4283	VGSTX.Volume_Log	Log of
4284	VGSTX.Volume_mva200	200 Day MA
4285	VGSTX.Volume_mva050	50 Day MA
4289	VGSTX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4291	VGSTX.Adjusted_SmoothDer	Derivative of Smoothed
4293	VGSTX.Adjusted_mva200	200 Day MA
4294	VGSTX.Adjusted_mva050	50 Day MA
4298	VFINX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4300	VFINX.Open_SmoothDer	Derivative of Smoothed
4302	VFINX.Open_mva200	200 Day MA
4303	VFINX.Open_mva050	50 Day MA
4307	VFINX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4309	VFINX.High_SmoothDer	Derivative of Smoothed
4311	VFINX.High_mva200	200 Day MA
4312	VFINX.High_mva050	50 Day MA
4316	VFINX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4318	VFINX.Low_SmoothDer	Derivative of Smoothed
4320	VFINX.Low_mva200	200 Day MA
4321	VFINX.Low_mva050	50 Day MA
4325	VFINX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4327	VFINX.Close_SmoothDer	Derivative of Smoothed
4329	VFINX.Close_mva200	200 Day MA
4330	VFINX.Close_mva050	50 Day MA
4331	VFINX.Volume_YoY	Year over Year
4332	VFINX.Volume_YoY4	4 Year over 4 Year
4333	VFINX.Volume_YoY5	5 Year over 5 Year
4334	VFINX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4335	VFINX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
4336	VFINX.Volume_SmoothDer	Derivative of Smoothed
4337	VFINX.Volume_Log	Log of
4338	VFINX.Volume_mva200	200 Day MA
4339	VFINX.Volume_mva050	50 Day MA
4343	VFINX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4345	VFINX.Adjusted_SmoothDer	Derivative of Smoothed
4347	VFINX.Adjusted_mva200	200 Day MA
4348	VFINX.Adjusted_mva050	50 Day MA
4356	VOE.Open_mva200	200 Day MA
4365	VOE.High_mva200	200 Day MA
4374	VOE.Low_mva200	200 Day MA
4383	VOE.Close_mva200	200 Day MA
4401	VOE.Adjusted_mva200	200 Day MA
4402	VOE.Adjusted_mva050	50 Day MA
4406	VOT.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4408	VOT.Open_SmoothDer	Derivative of Smoothed
4410	VOT.Open_mva200	200 Day MA
4411	VOT.Open_mva050	50 Day MA
4415	VOT.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4417	VOT.High_SmoothDer	Derivative of Smoothed
4419	VOT.High_mva200	200 Day MA
4420	VOT.High_mva050	50 Day MA
4424	VOT.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4426	VOT.Low_SmoothDer	Derivative of Smoothed
4428	VOT.Low_mva200	200 Day MA
4429	VOT.Low_mva050	50 Day MA
4433	VOT.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4435	VOT.Close_SmoothDer	Derivative of Smoothed
4437	VOT.Close_mva200	200 Day MA
4438	VOT.Close_mva050	50 Day MA
4451	VOT.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4453	VOT.Adjusted_SmoothDer	Derivative of Smoothed
4455	VOT.Adjusted_mva200	200 Day MA
4456	VOT.Adjusted_mva050	50 Day MA
4460	TMFGX.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4462	TMFGX.Open_SmoothDer	Derivative of Smoothed
4464	TMFGX.Open_mva200	200 Day MA
4465	TMFGX.Open_mva050	50 Day MA
4469	TMFGX.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4471	TMFGX.High_SmoothDer	Derivative of Smoothed
4473	TMFGX.High_mva200	200 Day MA
4474	TMFGX.High_mva050	50 Day MA
4478	TMFGX.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4480	TMFGX.Low_SmoothDer	Derivative of Smoothed
4482	TMFGX.Low_mva200	200 Day MA
4483	TMFGX.Low_mva050	50 Day MA
4487	TMFGX.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4489	TMFGX.Close_SmoothDer	Derivative of Smoothed
4491	TMFGX.Close_mva200	200 Day MA
4492	TMFGX.Close_mva050	50 Day MA
4493	TMFGX.Volume_YoY	Year over Year
4494	TMFGX.Volume_YoY4	4 Year over 4 Year
4495	TMFGX.Volume_YoY5	5 Year over 5 Year
4496	TMFGX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4497	TMFGX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
4498	TMFGX.Volume_SmoothDer	Derivative of Smoothed
4499	TMFGX.Volume_Log	Log of
4500	TMFGX.Volume_mva200	200 Day MA
4501	TMFGX.Volume_mva050	50 Day MA
4505	TMFGX.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4507	TMFGX.Adjusted_SmoothDer	Derivative of Smoothed
4509	TMFGX.Adjusted_mva200	200 Day MA
4510	TMFGX.Adjusted_mva050	50 Day MA
4518	IWM.Open_mva200	200 Day MA
4536	IWM.Low_mva200	200 Day MA
4563	IWM.Adjusted_mva200	200 Day MA
4568	ONEQ.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4570	ONEQ.Open_SmoothDer	Derivative of Smoothed
4572	ONEQ.Open_mva200	200 Day MA
4573	ONEQ.Open_mva050	50 Day MA
4577	ONEQ.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4579	ONEQ.High_SmoothDer	Derivative of Smoothed
4581	ONEQ.High_mva200	200 Day MA
4582	ONEQ.High_mva050	50 Day MA
4586	ONEQ.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4588	ONEQ.Low_SmoothDer	Derivative of Smoothed
4590	ONEQ.Low_mva200	200 Day MA
4591	ONEQ.Low_mva050	50 Day MA
4595	ONEQ.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4597	ONEQ.Close_SmoothDer	Derivative of Smoothed
4599	ONEQ.Close_mva200	200 Day MA
4600	ONEQ.Close_mva050	50 Day MA
4613	ONEQ.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4615	ONEQ.Adjusted_SmoothDer	Derivative of Smoothed
4617	ONEQ.Adjusted_mva200	200 Day MA
4618	ONEQ.Adjusted_mva050	50 Day MA
4626	HAINX.Open_mva200	200 Day MA
4627	HAINX.Open_mva050	50 Day MA
4635	HAINX.High_mva200	200 Day MA
4636	HAINX.High_mva050	50 Day MA
4644	HAINX.Low_mva200	200 Day MA
4645	HAINX.Low_mva050	50 Day MA
4653	HAINX.Close_mva200	200 Day MA
4654	HAINX.Close_mva050	50 Day MA
4655	HAINX.Volume_YoY	Year over Year
4656	HAINX.Volume_YoY4	4 Year over 4 Year
4657	HAINX.Volume_YoY5	5 Year over 5 Year
4658	HAINX.Volume_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4659	HAINX.Volume_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15)
4660	HAINX.Volume_SmoothDer	Derivative of Smoothed
4661	HAINX.Volume_Log	Log of
4662	HAINX.Volume_mva200	200 Day MA
4663	HAINX.Volume_mva050	50 Day MA
4671	HAINX.Adjusted_mva200	200 Day MA
4672	HAINX.Adjusted_mva050	50 Day MA
4680	VEU.Open_mva200	200 Day MA
4689	VEU.High_mva200	200 Day MA
4698	VEU.Low_mva200	200 Day MA
4707	VEU.Close_mva200	200 Day MA
4725	VEU.Adjusted_mva200	200 Day MA
4788	IVOO.Open_mva200	200 Day MA
4797	IVOO.High_mva200	200 Day MA
4806	IVOO.Low_mva200	200 Day MA
4815	IVOO.Close_mva200	200 Day MA
4823	IVOO.Volume_Log	Log of
4833	IVOO.Adjusted_mva200	200 Day MA
4838	VO.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4842	VO.Open_mva200	200 Day MA
4843	VO.Open_mva050	50 Day MA
4847	VO.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4851	VO.High_mva200	200 Day MA
4852	VO.High_mva050	50 Day MA
4856	VO.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4860	VO.Low_mva200	200 Day MA
4861	VO.Low_mva050	50 Day MA
4865	VO.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4869	VO.Close_mva200	200 Day MA
4870	VO.Close_mva050	50 Day MA
4877	VO.Volume_Log	Log of
4883	VO.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4887	VO.Adjusted_mva200	200 Day MA
4888	VO.Adjusted_mva050	50 Day MA
4892	CZA.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4896	CZA.Open_mva200	200 Day MA
4897	CZA.Open_mva050	50 Day MA
4901	CZA.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4905	CZA.High_mva200	200 Day MA
4906	CZA.High_mva050	50 Day MA
4910	CZA.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4914	CZA.Low_mva200	200 Day MA
4915	CZA.Low_mva050	50 Day MA
4919	CZA.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4923	CZA.Close_mva200	200 Day MA
4924	CZA.Close_mva050	50 Day MA
4931	CZA.Volume_Log	Log of
4937	CZA.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
4941	CZA.Adjusted_mva200	200 Day MA
4942	CZA.Adjusted_mva050	50 Day MA
4950	VYM.Open_mva200	200 Day MA
4959	VYM.High_mva200	200 Day MA
4968	VYM.Low_mva200	200 Day MA
4969	VYM.Low_mva050	50 Day MA
4977	VYM.Close_mva200	200 Day MA
4978	VYM.Close_mva050	50 Day MA
4995	VYM.Adjusted_mva200	200 Day MA
4996	VYM.Adjusted_mva050	50 Day MA
5000	ACWI.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5004	ACWI.Open_mva200	200 Day MA
5005	ACWI.Open_mva050	50 Day MA
5009	ACWI.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5013	ACWI.High_mva200	200 Day MA
5014	ACWI.High_mva050	50 Day MA
5018	ACWI.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5022	ACWI.Low_mva200	200 Day MA
5023	ACWI.Low_mva050	50 Day MA
5027	ACWI.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5031	ACWI.Close_mva200	200 Day MA
5032	ACWI.Close_mva050	50 Day MA
5045	ACWI.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5049	ACWI.Adjusted_mva200	200 Day MA
5050	ACWI.Adjusted_mva050	50 Day MA
5058	SLY.Open_mva200	200 Day MA
5067	SLY.High_mva200	200 Day MA
5076	SLY.Low_mva200	200 Day MA
5085	SLY.Close_mva200	200 Day MA
5093	SLY.Volume_Log	Log of
5103	SLY.Adjusted_mva200	200 Day MA
5108	QQQ.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5110	QQQ.Open_SmoothDer	Derivative of Smoothed
5112	QQQ.Open_mva200	200 Day MA
5113	QQQ.Open_mva050	50 Day MA
5117	QQQ.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5119	QQQ.High_SmoothDer	Derivative of Smoothed
5121	QQQ.High_mva200	200 Day MA
5122	QQQ.High_mva050	50 Day MA
5126	QQQ.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5128	QQQ.Low_SmoothDer	Derivative of Smoothed
5130	QQQ.Low_mva200	200 Day MA
5131	QQQ.Low_mva050	50 Day MA
5135	QQQ.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5137	QQQ.Close_SmoothDer	Derivative of Smoothed
5139	QQQ.Close_mva200	200 Day MA
5140	QQQ.Close_mva050	50 Day MA
5153	QQQ.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5155	QQQ.Adjusted_SmoothDer	Derivative of Smoothed
5157	QQQ.Adjusted_mva200	200 Day MA
5158	QQQ.Adjusted_mva050	50 Day MA
5162	HYMB.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5164	HYMB.Open_SmoothDer	Derivative of Smoothed
5166	HYMB.Open_mva200	200 Day MA
5171	HYMB.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5173	HYMB.High_SmoothDer	Derivative of Smoothed
5175	HYMB.High_mva200	200 Day MA
5180	HYMB.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5182	HYMB.Low_SmoothDer	Derivative of Smoothed
5184	HYMB.Low_mva200	200 Day MA
5189	HYMB.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5191	HYMB.Close_SmoothDer	Derivative of Smoothed
5193	HYMB.Close_mva200	200 Day MA
5201	HYMB.Volume_Log	Log of
5207	HYMB.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5209	HYMB.Adjusted_SmoothDer	Derivative of Smoothed
5211	HYMB.Adjusted_mva200	200 Day MA
5218	GOLD.Open_SmoothDer	Derivative of Smoothed
5219	GOLD.Open_Log	Log of
5227	GOLD.High_SmoothDer	Derivative of Smoothed
5236	GOLD.Low_SmoothDer	Derivative of Smoothed
5245	GOLD.Close_SmoothDer	Derivative of Smoothed
5255	GOLD.Volume_Log	Log of
5263	GOLD.Adjusted_SmoothDer	Derivative of Smoothed
5273	BKR.Open_Log	Log of
5309	BKR.Volume_Log	Log of
5328	SLB.Open_mva200	200 Day MA
5346	SLB.Low_mva200	200 Day MA
5381	HAL.Open_Log	Log of
5417	HAL.Volume_Log	Log of
5435	IP.Open_Log	Log of
5436	IP.Open_mva200	200 Day MA
5445	IP.High_mva200	200 Day MA
5454	IP.Low_mva200	200 Day MA
5463	IP.Close_mva200	200 Day MA
5481	IP.Adjusted_mva200	200 Day MA
5486	PKG.Open_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5490	PKG.Open_mva200	200 Day MA
5495	PKG.High_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5499	PKG.High_mva200	200 Day MA
5504	PKG.Low_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5508	PKG.Low_mva200	200 Day MA
5513	PKG.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5517	PKG.Close_mva200	200 Day MA
5524	PKG.Volume_SmoothDer	Derivative of Smoothed
5531	PKG.Adjusted_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
5535	PKG.Adjusted_mva200	200 Day MA
5542	UPS.Open_SmoothDer	Derivative of Smoothed
5544	UPS.Open_mva200	200 Day MA
5551	UPS.High_SmoothDer	Derivative of Smoothed
5553	UPS.High_mva200	200 Day MA
5560	UPS.Low_SmoothDer	Derivative of Smoothed
5562	UPS.Low_mva200	200 Day MA
5569	UPS.Close_SmoothDer	Derivative of Smoothed
5571	UPS.Close_mva200	200 Day MA
5587	UPS.Adjusted_SmoothDer	Derivative of Smoothed
5589	UPS.Adjusted_mva200	200 Day MA
5598	FDX.Open_mva200	200 Day MA
5607	FDX.High_mva200	200 Day MA
5616	FDX.Low_mva200	200 Day MA
5625	FDX.Close_mva200	200 Day MA
5632	FDX.Volume_SmoothDer	Derivative of Smoothed
5643	FDX.Adjusted_mva200	200 Day MA
5651	T.Open_Log	Log of
5704	VZ.Open_SmoothDer	Derivative of Smoothed
5713	VZ.High_SmoothDer	Derivative of Smoothed
5722	VZ.Low_SmoothDer	Derivative of Smoothed
5731	VZ.Close_SmoothDer	Derivative of Smoothed
5749	VZ.Adjusted_SmoothDer	Derivative of Smoothed
5765	MULTPLSP500PERATIOMONTH_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 TTM P/E
5767	MULTPLSP500PERATIOMONTH_SmoothDer	Derivative of Smoothed S&P 500 TTM P/E
5771	MULTPLSP500SALESQUARTER_YoY	S&P 500 TTM Sales (Not Inflation Adjusted) Year over Year
5777	MULTPLSP500SALESQUARTER_Log	Log of S&P 500 TTM Sales (Not Inflation Adjusted)
5778	MULTPLSP500SALESQUARTER_mva200	S&P 500 TTM Sales (Not Inflation Adjusted) 200 Day MA
5779	MULTPLSP500SALESQUARTER_mva050	S&P 500 TTM Sales (Not Inflation Adjusted) 50 Day MA
5794	MULTPLSP500DIVMONTH_SmoothDer	Derivative of Smoothed S&P 500 Dividend by Month (Inflation Adjusted)
5805	CHRISCMEHG1_mva200	Copper Futures, Continuous Contract #1 (HG1) (Front Month) 200 Day MA
5807	WWDIWLDISAIRGOODMTK1_YoY	Air transport, freight Year over Year
5813	WWDIWLDISAIRGOODMTK1_Log	Log of Air transport, freight
5814	WWDIWLDISAIRGOODMTK1_mva200	Air transport, freight 200 Day MA
5815	WWDIWLDISAIRGOODMTK1_mva050	Air transport, freight 50 Day MA
5834	PETA143B00001M_YoY	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly Year over Year
5835	PETA143B00001M_YoY4	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 4 Year over 4 Year
5836	PETA143B00001M_YoY5	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 5 Year over 5 Year
5840	PETA143B00001M_Log	Log of U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
5841	PETA143B00001M_mva200	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 200 Day MA
5842	PETA143B00001M_mva050	U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 50 Day MA
5848	PETA133B00001M_SmoothDer	Derivative of Smoothed U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly
5853	TOTALOGNRPUSM_YoY4	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 4 Year over 4 Year
5858	TOTALOGNRPUSM_Log	Log of Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly
5859	TOTALOGNRPUSM_mva200	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 200 Day MA
5860	TOTALOGNRPUSM_mva050	Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 50 Day MA
5862	TOTALPANRPUSM_YoY4	Crude Oil Rotary Rigs in Operation, Monthly 4 Year over 4 Year
5867	TOTALPANRPUSM_Log	Log of Crude Oil Rotary Rigs in Operation, Monthly
5868	TOTALPANRPUSM_mva200	Crude Oil Rotary Rigs in Operation, Monthly 200 Day MA
5869	TOTALPANRPUSM_mva050	Crude Oil Rotary Rigs in Operation, Monthly 50 Day MA
5876	TOTALNGNRPUSM_Log	Log of Natural Gas Rotary Rigs in Operation, Monthly
5877	TOTALNGNRPUSM_mva200	Natural Gas Rotary Rigs in Operation, Monthly 200 Day MA
5878	TOTALNGNRPUSM_mva050	Natural Gas Rotary Rigs in Operation, Monthly 50 Day MA
5885	BKRTotal_Log	Log of Total Rig Count
5886	BKRTotal_mva200	Total Rig Count 200 Day MA
5887	BKRTotal_mva050	Total Rig Count 50 Day MA
5894	BKRGas_Log	Log of Gas Rig Count
5895	BKRGas_mva200	Gas Rig Count 200 Day MA
5896	BKRGas_mva050	Gas Rig Count 50 Day MA
5903	BKROil_Log	Log of Oil Rig Count
5905	BKROil_mva050	Oil Rig Count 50 Day MA
5906	FARMINCOME_YoY	Net Farm Income Year over Year
5912	FARMINCOME_Log	Log of Net Farm Income
5913	FARMINCOME_mva200	Net Farm Income 200 Day MA
5914	FARMINCOME_mva050	Net Farm Income 50 Day MA
5921	OPEARNINGSPERSHARE_Log	Log of Operating Earnings per Share
5922	OPEARNINGSPERSHARE_mva200	Operating Earnings per Share 200 Day MA
5923	OPEARNINGSPERSHARE_mva050	Operating Earnings per Share 50 Day MA
5930	AREARNINGSPERSHARE_Log	Log of As-Reported Earnings per Share
5931	AREARNINGSPERSHARE_mva200	As-Reported Earnings per Share 200 Day MA
5932	AREARNINGSPERSHARE_mva050	As-Reported Earnings per Share 50 Day MA
5933	CASHDIVIDENDSPERSHR_YoY	Cash Dividends per Share Year over Year
5939	CASHDIVIDENDSPERSHR_Log	Log of Cash Dividends per Share
5940	CASHDIVIDENDSPERSHR_mva200	Cash Dividends per Share 200 Day MA
5941	CASHDIVIDENDSPERSHR_mva050	Cash Dividends per Share 50 Day MA
5957	BOOKVALPERSHR_Log	Log of Book value per Share
5958	BOOKVALPERSHR_mva200	Book value per Share 200 Day MA
5959	BOOKVALPERSHR_mva050	Book value per Share 50 Day MA
5975	PRICE_Log	Log of Price
5976	PRICE_mva200	Price 200 Day MA
5977	PRICE_mva050	Price 50 Day MA
5978	OPEARNINGSTTM_YoY	TTM Operating Earnings Year over Year
5984	OPEARNINGSTTM_Log	Log of TTM Operating Earnings
5985	OPEARNINGSTTM_mva200	TTM Operating Earnings 200 Day MA
5986	OPEARNINGSTTM_mva050	TTM Operating Earnings 50 Day MA
5987	AREARNINGSTTM_YoY	TTM Reported Earnings Year over Year
5993	AREARNINGSTTM_Log	Log of TTM Reported Earnings
5994	AREARNINGSTTM_mva200	TTM Reported Earnings 200 Day MA
5995	AREARNINGSTTM_mva050	TTM Reported Earnings 50 Day MA
6003	FINRAMarginDebt_mva200	Margin Debt 200 Day MA
6014	OCCEquityVolume_YoY	Equity Options Volume Year over Year
6020	OCCEquityVolume_Log	Log of Equity Options Volume
6021	OCCEquityVolume_mva200	Equity Options Volume 200 Day MA
6022	OCCEquityVolume_mva050	Equity Options Volume 50 Day MA
6023	OCCNonEquityVolume_YoY	Non-Equity Options Volume Year over Year
6029	OCCNonEquityVolume_Log	Log of Non-Equity Options Volume
6030	OCCNonEquityVolume_mva200	Non-Equity Options Volume 200 Day MA
6031	OCCNonEquityVolume_mva050	Non-Equity Options Volume 50 Day MA
6044	BUSLOANS.minus.BUSLOANSNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Montlhy) SA - NSA
6046	BUSLOANS.minus.BUSLOANSNSA_SmoothDer	Derivative of Smoothed Business Loans (Montlhy) SA - NSA
6047	BUSLOANS.minus.BUSLOANSNSA_Log	Log of Business Loans (Montlhy) SA - NSA
6049	BUSLOANS.minus.BUSLOANSNSA_mva050	Business Loans (Montlhy) SA - NSA 50 Day MA
6053	BUSLOANS.minus.BUSLOANSNSA.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Montlhy) SA - NSA divided by GDP
6055	BUSLOANS.minus.BUSLOANSNSA.by.GDP_SmoothDer	Derivative of Smoothed Business Loans (Montlhy) SA - NSA divided by GDP
6056	BUSLOANS.minus.BUSLOANSNSA.by.GDP_Log	Log of Business Loans (Montlhy) SA - NSA divided by GDP
6058	BUSLOANS.minus.BUSLOANSNSA.by.GDP_mva050	Business Loans (Montlhy) SA - NSA divided by GDP 50 Day MA
6059	BUSLOANS.by.GDP_YoY	Business Loans Normalized by GDP Year over Year
6086	BUSLOANSNSA.by.GDP_YoY	Business Loans Normalized by GDP Year over Year
6132	W875RX1.by.GDP_YoY4	Real Personal Income Normalized by GDP 4 Year over 4 Year
6133	W875RX1.by.GDP_YoY5	Real Personal Income Normalized by GDP 5 Year over 5 Year
6134	W875RX1.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Real Personal Income Normalized by GDP
6136	W875RX1.by.GDP_SmoothDer	Derivative of Smoothed Real Personal Income Normalized by GDP
6140	A065RC1A027NBEA.by.GDP_YoY	Personal Income (NSA) Normalized by GDP Year over Year
6145	A065RC1A027NBEA.by.GDP_SmoothDer	Derivative of Smoothed Personal Income (NSA) Normalized by GDP
6159	A053RC1Q027SBEA.by.GDP_YoY4	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 4 Year over 4 Year
6164	A053RC1Q027SBEA.by.GDP_Log	Log of National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
6165	A053RC1Q027SBEA.by.GDP_mva200	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 200 Day MA
6166	A053RC1Q027SBEA.by.GDP_mva050	National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 50 Day MA
6173	CPROFIT.by.GDP_Log	Log of National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
6174	CPROFIT.by.GDP_mva200	National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 200 Day MA
6175	CPROFIT.by.GDP_mva050	National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 50 Day MA
6176	CONSUMERNSA.by.GDP_YoY	Consumer Loans Not Seasonally Adjusted divided by GDP Year over Year
6179	CONSUMERNSA.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans Not Seasonally Adjusted divided by GDP
6181	CONSUMERNSA.by.GDP_SmoothDer	Derivative of Smoothed Consumer Loans Not Seasonally Adjusted divided by GDP
6182	CONSUMERNSA.by.GDP_Log	Log of Consumer Loans Not Seasonally Adjusted divided by GDP
6184	CONSUMERNSA.by.GDP_mva050	Consumer Loans Not Seasonally Adjusted divided by GDP 50 Day MA
6185	RREACBM027NBOG.by.GDP_YoY	Residental Real Estate Loans (Monthly, NSA) divided by GDP Year over Year
6188	RREACBM027NBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Monthly, NSA) divided by GDP
6190	RREACBM027NBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Monthly, NSA) divided by GDP
6194	RREACBM027SBOG.by.GDP_YoY	Residental Real Estate Loans (Monthly, SA) divided by GDP Year over Year
6199	RREACBM027SBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Monthly, SA) divided by GDP
6203	RREACBW027SBOG.by.GDP_YoY	Residental Real Estate Loans (Weekly, SA) divided by GDP Year over Year
6204	RREACBW027SBOG.by.GDP_YoY4	Residental Real Estate Loans (Weekly, SA) divided by GDP 4 Year over 4 Year
6206	RREACBW027SBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, SA) divided by GDP
6208	RREACBW027SBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Weekly, SA) divided by GDP
6212	RREACBW027NBOG.by.GDP_YoY	Residental Real Estate Loans (Weekly, NSA) divided by GDP Year over Year
6213	RREACBW027NBOG.by.GDP_YoY4	Residental Real Estate Loans (Weekly, NSA) divided by GDP 4 Year over 4 Year
6215	RREACBW027NBOG.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, NSA) divided by GDP
6217	RREACBW027NBOG.by.GDP_SmoothDer	Derivative of Smoothed Residental Real Estate Loans (Weekly, NSA) divided by GDP
6233	DGORDER.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Durable Goods (Monthly, NSA) divided by GDP
6235	DGORDER.by.GDP_SmoothDer	Derivative of Smoothed Durable Goods (Monthly, NSA) divided by GDP
6237	DGORDER.by.GDP_mva200	Durable Goods (Monthly, NSA) divided by GDP 200 Day MA
6239	ASHMA.by.GDP_YoY	Home Mortgages (Quarterly, NSA) divided by GDP Year over Year
6240	ASHMA.by.GDP_YoY4	Home Mortgages (Quarterly, NSA) divided by GDP 4 Year over 4 Year
6244	ASHMA.by.GDP_SmoothDer	Derivative of Smoothed Home Mortgages (Quarterly, NSA) divided by GDP
6248	ASHMA.INTEREST_YoY	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Year over Year
6255	ASHMA.INTEREST_mva200	Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6257	ASHMA.INTEREST.by.GDP_YoY	Year over Year
6269	CONSUMERNSA.INTEREST_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6271	CONSUMERNSA.INTEREST_SmoothDer	Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6272	CONSUMERNSA.INTEREST_Log	Log of Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6273	CONSUMERNSA.INTEREST_mva200	Consumer Loans (Not Seasonally Adjusted) Interest Burdens 200 Day MA
6274	CONSUMERNSA.INTEREST_mva050	Consumer Loans (Not Seasonally Adjusted) Interest Burdens 50 Day MA
6277	CONSUMERNSA.INTEREST.by.GDP_YoY5	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 5 Year over 5 Year
6278	CONSUMERNSA.INTEREST.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6280	CONSUMERNSA.INTEREST.by.GDP_SmoothDer	Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6281	CONSUMERNSA.INTEREST.by.GDP_Log	Log of Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6283	CONSUMERNSA.INTEREST.by.GDP_mva050	Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 50 Day MA
6289	TOTLNNSA_SmoothDer	Derivative of Smoothed Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
6293	TOTLNNSA.by.GDP_YoY	Total Loans Not Seasonally Adjusted divided by GDP Year over Year
6298	TOTLNNSA.by.GDP_SmoothDer	Derivative of Smoothed Total Loans Not Seasonally Adjusted divided by GDP
6322	WRESBAL.by.GDP_YoY5	Reserve Balances with Federal Reserve Banks Divided by GDP 5 Year over 5 Year
6323	WRESBAL.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks Divided by GDP
6324	WRESBAL.by.GDP_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Reserve Balances with Federal Reserve Banks Divided by GDP
6326	WRESBAL.by.GDP_Log	Log of Reserve Balances with Federal Reserve Banks Divided by GDP
6327	WRESBAL.by.GDP_mva200	Reserve Balances with Federal Reserve Banks Divided by GDP 200 Day MA
6328	WRESBAL.by.GDP_mva050	Reserve Balances with Federal Reserve Banks Divided by GDP 50 Day MA
6338	WLRRAL.by.GDP_YoY	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP Year over Year
6340	WLRRAL.by.GDP_YoY5	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 5 Year over 5 Year
6341	WLRRAL.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6342	WLRRAL.by.GDP_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6343	WLRRAL.by.GDP_SmoothDer	Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6344	WLRRAL.by.GDP_Log	Log of Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6345	WLRRAL.by.GDP_mva200	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 200 Day MA
6346	WLRRAL.by.GDP_mva050	Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 50 Day MA
6354	SOFR99.minus.SOFR1_mva200	Secured Overnight Financing Rate: 99th Percentile - 1st Percentile 200 Day MA
6362	EXPCH.minus.IMPCH_Log	Log of U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
6368	EXPMX.minus.IMPMX_Smooth	Savitsky-Golay Smoothed (p=3, n=365)
6371	EXPMX.minus.IMPMX_Log	Log of
6373	EXPMX.minus.IMPMX_mva050	50 Day MA
6375	SRPSABSNNCB.by.GDP_YoY4	Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 4 Year over 4 Year
6379	SRPSABSNNCB.by.GDP_SmoothDer	Derivative of Smoothed Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6380	SRPSABSNNCB.by.GDP_Log	Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6383	ASTLL.by.GDP_YoY	All sectors; total loans; liability, Level (NSA) Divided by GDP Year over Year
6388	ASTLL.by.GDP_SmoothDer	Derivative of Smoothed All sectors; total loans; liability, Level (NSA) Divided by GDP
6392	ASFMA.by.GDP_YoY	All sectors; farm mortgages; asset, Level (NSA) Divided by GDP Year over Year
6397	ASFMA.by.GDP_SmoothDer	Derivative of Smoothed All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
6402	ASFMA.by.ASTLL_YoY4	All sectors; total loans Divided by farm mortgages 4 Year over 4 Year
6406	ASFMA.by.ASTLL_SmoothDer	Derivative of Smoothed All sectors; total loans Divided by farm mortgages
6407	ASFMA.by.ASTLL_Log	Log of All sectors; total loans Divided by farm mortgages
6409	ASFMA.by.ASTLL_mva050	All sectors; total loans Divided by farm mortgages 50 Day MA
6410	ASFMA.INTEREST_YoY	Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Year over Year
6417	ASFMA.INTEREST_mva200	Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6419	ASFMA.INTEREST.by.GDP_YoY	Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP Year over Year
6428	FARMINCOME.by.GDP_YoY	Farm Income (Annual, NSA) Divided by GDP Year over Year
6433	FARMINCOME.by.GDP_SmoothDer	Derivative of Smoothed Farm Income (Annual, NSA) Divided by GDP
6439	BOGMBASE.by.GDP_YoY5	BOGMBASE Divided by GDP 5 Year over 5 Year
6443	BOGMBASE.by.GDP_Log	Log of BOGMBASE Divided by GDP
6444	BOGMBASE.by.GDP_mva200	BOGMBASE Divided by GDP 200 Day MA
6445	BOGMBASE.by.GDP_mva050	BOGMBASE Divided by GDP 50 Day MA
6449	WALCL.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets Divided by GDP
6450	WALCL.by.GDP_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) All Federal Reserve Banks: Total Assets Divided by GDP
6451	WALCL.by.GDP_SmoothDer	Derivative of Smoothed All Federal Reserve Banks: Total Assets Divided by GDP
6452	WALCL.by.GDP_Log	Log of All Federal Reserve Banks: Total Assets Divided by GDP
6453	WALCL.by.GDP_mva200	All Federal Reserve Banks: Total Assets Divided by GDP 200 Day MA
6454	WALCL.by.GDP_mva050	All Federal Reserve Banks: Total Assets Divided by GDP 50 Day MA
6455	ECBASSETS.by.EUNNGDP_YoY	Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP Year over Year
6470	DGS30TO10_Log	Log of Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
6479	DGS10TO1_Log	Log of Yield Curve, 10 and 1 Year Treasury (DGS10-DGS1)
6488	DGS10TO2_Log	Log of Yield Curve, 10 and 2 Year Treasury (DGS10-DGS2)
6497	DGS10TOTB3MS_Log	Log of Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
6506	DGS10TODTB3_Log	Log of Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
6512	DGS10ByAAA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) AAA ratio to 10 year treasury (AAA/DGS10)
6514	DGS10ByAAA_SmoothDer	Derivative of Smoothed AAA ratio to 10 year treasury (AAA/DGS10)
6518	LNU03000000BYPOPTHM_YoY	Unemployment level (NSA) / Population Year over Year
6539	NPPTTLBYPOPTHM_Smooth	Savitsky-Golay Smoothed (p=3, n=365) ADP Private Employment / Population
6541	NPPTTLBYPOPTHM_SmoothDer	Derivative of Smoothed ADP Private Employment / Population
6542	NPPTTLBYPOPTHM_Log	Log of ADP Private Employment / Population
6543	NPPTTLBYPOPTHM_mva200	ADP Private Employment / Population 200 Day MA
6544	NPPTTLBYPOPTHM_mva050	ADP Private Employment / Population 50 Day MA
6570	CHRISCMEHG1.by.CPIAUCSL_mva200	Copper, $/lb, Normalized by consumer price index 200 Day MA
6579	DCOILBRENTEU.by.PPIACO_mva200	Crude Oil - Brent, $/bbl, Normalized by producer price index c.o. 200 Day MA
6587	DCOILWTICO.by.PPIACO_Log	Log of Crude Oil - WTI, $/bbl, Normalized by producer price index c.o.
6588	DCOILWTICO.by.PPIACO_mva200	Crude Oil - WTI, $/bbl, Normalized by producer price index c.o. 200 Day MA
6595	GOLDAMGBD228NLBM.by.PPIACO_SmoothDer	Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by commodities producer price index
6604	GOLDAMGBD228NLBM.by.CPIAUCSL_SmoothDer	Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by consumer price index
6611	GOLDAMGBD228NLBM.by.GDP_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Gold, USD/Troy OUnce, Normalized by GDP
6613	GOLDAMGBD228NLBM.by.GDP_SmoothDer	Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by GDP
6624	GSG.Close.by.GDPDEF_mva200	GSCI Commodity-Indexed Trust, Normalized by GDP def 200 Day MA
6642	GDPBYPOPTHM_mva200	GDP/Population 200 Day MA
6665	GSPC.CloseBYMDY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365) GSPC by MDY
6667	GSPC.CloseBYMDY.Close_SmoothDer	Derivative of Smoothed GSPC by MDY
6670	GSPC.CloseBYMDY.Close_mva050	GSPC by MDY 50 Day MA
6674	QQQ.CloseBYMDY.Close_Smooth	Savitsky-Golay Smoothed (p=3, n=365) QQQ by MDY
6675	QQQ.CloseBYMDY.Close_Smooth.short	Savitsky-Golay Smoothed (p=3, n=15) QQQ by MDY
6676	QQQ.CloseBYMDY.Close_SmoothDer	Derivative of Smoothed QQQ by MDY
6679	QQQ.CloseBYMDY.Close_mva050	QQQ by MDY 50 Day MA
6686	GSPC.DailySwing_Log	Log of S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
6692	GSPC.Open.by.GDPDEF_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Open divided by GDP deflator
6694	GSPC.Open.by.GDPDEF_SmoothDer	Derivative of Smoothed S&P 500 (^GSPC) Open divided by GDP deflator
6696	GSPC.Open.by.GDPDEF_mva200	S&P 500 (^GSPC) Open divided by GDP deflator 200 Day MA
6697	GSPC.Open.by.GDPDEF_mva050	S&P 500 (^GSPC) Open divided by GDP deflator 50 Day MA
6701	GSPC.Close.by.GDPDEF_Smooth	Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Close divided by GDP deflator
6703	GSPC.Close.by.GDPDEF_SmoothDer	Derivative of Smoothed S&P 500 (^GSPC) Close divided by GDP deflator
6705	GSPC.Close.by.GDPDEF_mva200	S&P 500 (^GSPC) Close divided by GDP deflator 200 Day MA
6706	GSPC.Close.by.GDPDEF_mva050	S&P 500 (^GSPC) Close divided by GDP deflator 50 Day MA
6710	HNFSUSNSA.minus.HSN1FNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) Houses for sale - houses sold
6713	HNFSUSNSA.minus.HSN1FNSA_Log	Log of Houses for sale - houses sold
6714	HNFSUSNSA.minus.HSN1FNSA_mva200	Houses for sale - houses sold 200 Day MA
6715	HNFSUSNSA.minus.HSN1FNSA_mva050	Houses for sale - houses sold 50 Day MA
6728	MSPUS.times.HNFSUSNSA_Smooth	Savitsky-Golay Smoothed (p=3, n=365) New privately owned 1-family units for sale times median price
6731	MSPUS.times.HNFSUSNSA_Log	Log of New privately owned 1-family units for sale times median price
6732	MSPUS.times.HNFSUSNSA_mva200	New privately owned 1-family units for sale times median price 200 Day MA
6733	MSPUS.times.HNFSUSNSA_mva050	New privately owned 1-family units for sale times median price 50 Day MA
6734	TOTLNNSA.PLUS.WRESBAL	Total Loans Plus All Reserves (TOTLNNSA + WRESBAL)
6735	TOTLLNSA.PLUS.WRESBAL	Total Loans Plus All Reserves (TOTLLNSA + WRESBAL)
6741	GSPC.Open_mva050_mva200_sig	Sell Signal S&P 500 50 SMA - 200 SMA
6742	MULTPLSP500PERATIOMONTH_Mean	S&P 500 TTM P/E Average (Excludes Values Greater Than 50)

Equities

S&P 500 SMA Trends

Take a look at recent activity in the equities market. This section looks at the S&P 500 close value to the 50 day and 200 day simple moving average (SMA).

This is a longer view for the S&P 500 trend.

Equity indexes normalized by GDP

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

The last two years compare favorably with the period around the late 1950’s. Need to dig into this one.

datay <- "GSPC.Close"
ylim <- c(2000, d.GSPC.max)
my.data <- plotSimilarPeriods(df.data, dfRecession, df.symbols, datay, ylim, i.window = 60)
my.data[[1]]

Look at how the different segments of the market move

datay <- "GSPC.CloseBYMDY.Close_YoY"
ylim <- c(-50, 75)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

datay <- "GSPC.CloseBYMDY.Close"
ylim <- c(0, 20)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

S&P 500 Normalized moving average

Look at moving average relationship by dividing the S&P 500 open price by the 200 day SMA.

datay <- "GSPC.Open_mva200_Norm"
ylim <- c(50, 125)
dt.start = as.Date('2008-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Crossovers

Look at the 50 DMA versus 200 DMA, often used as a technical indicator of market direction.

datay <- "GSPC.Open_mva050_mva200"
ylim <- c(-200, 200)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

datay <- "GSPC.Open_mva050_mva200_sig "
ylim <- c(0.0, 1.0)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

S&P 500 TTM P/E

Take a look at some of the earnings trends from SilverBlatt’s sheet.

## New names:
## * `` -> ...2
## * `` -> ...5
## * `` -> ...8
## New names:
## * `` -> ...2
## * `` -> ...5
## * `` -> ...8
## New names:
## * `` -> ...2
## * `` -> ...5
## * `` -> ...8

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...

Take a longer look back at as-reported and operating earnings

Market prices can out-run earnings so take a look at price to earnings.

Focus on some of the more recent activity

S&P 500 Sales

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start <- as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Unit Profits

The series peaks in the middle of a bull market.

S&P 500 dividends

12-month real dividend per share inflation adjusted November, 2018 dollars. Data courtesy Standard & Poor’s and Robert Shiller.

https://www.quandl.com/data/MULTPL/SP500_DIV_MONTH-S-P-500-Dividend-by-Month

Evaluate year over year dividend growth.

Real value dividend growth.

datay <- "MULTPLSP500DIVMONTH_YoY"
ylim <- c(-40, 20)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 dividend yield (12 month dividend per share)/price. Yields following September 2018 (including the current yield) are estimated based on 12 month dividends through September 2018, as reported by S&P. Sources: Standard & Poor’s for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.

https://www.quandl.com/data/MULTPL/SP500_DIV_YIELD_MONTH-S-P-500-Dividend-Yield-by-Month

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(0, 12)
dtStart = as.Date('1950-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(1, 4)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 Volume

The log of the S&P volume has some interesting patterns, but nothing that seems to help with a recession indicator.

That is one spiky data series. Not sure there is a lot to help us here.

Russell 2000

Take a look at recent activity in the small cap market.

S&P 500 to Rusell 2000

Thirty day movement

Correlation

## Warning in max.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to max;
## returning -Inf

## Warning in min.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

S&P 500 to MDY (Mid-cap) 2000 Correlation

datay1 <- "RLG.Open"
ylim1 <- c(0, 2500)

datay2 <- "MDY.Open"
ylim2 <- c(0, 500)

dtStart <- as.Date("1jan2003","%d%b%Y")

w <- 30
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

## Warning in max.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to max;
## returning -Inf

## Warning in min.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

Dividend Stocks

This is an interesting series, they should perform better through the recessions. Unfortunately they are short lived so there is not much data so this is more of a place holder for now.

datay <- "NOBL.Open"
ylim <- c(40, 110)
dt.start <- as.Date('2014-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Margin and option data

NYSE Margin Debt

Taking a look at margin debt. NYXDATA stopped providing NYSE margin debt data on Dec 2017. Data is available from FINRA, but it includes more accounts than the data did for NYXdata. I stitched togeter the data sets: data after Jan 2010 include NYSE+Others, data prior is just NYSE account data scaled up to match the FINRA data.

It tends to creep up when there is a frenzy in the stock market.

datay <- "FINRAMarginDebt_Log"
ylim <- c(5, 15)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Take a close look at recent activity

Sometimes it is more helpful to view year over year growth.

More near-term trend.

Take a look at some of the correlations

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

Comparison to the Russell 2000

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "RLG.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

OCC Options Volumes

See what is happening with the options volumes for equities. (From: https://www.theocc.com/webapps/historical-volume-query)

Looks like options on non-equity co-occurs with peaks/troughs?.

Market Volatility

Take a look at some of the indications of market volatility

CBOE VIX

As markets become complacent (low VIX) and high values, peaks often occur.

Compare the VIX to some of the ETF’s out there.

There

Not much predictive in VIX, take a quick look at the smoothed derivative.

S&P Daily Swings

Daily changes in the S&P should correlate well with the VIX.

More of a correlating series than a predictor.

Employment and payrolls

Unemployment rates

Unemployment rates will probably be useful, let’s take a look at the U-3. The data is a little noisy so there is also a smoothed version plotted. There seems to be a relationship between the unemployment rate and the recessions, but it could be a lagging indicator. This will be explored a little bit more later.

Looking at the unemployment rate, the eye is drawn to the rise and fall of the data, this suggests that the derivative might be helpful as well. The figure below shows the results, using a Savitzky-Golay FIR filter. It looks like the unemployment rate peaks in the middel of the recession. That peak might be a good buy signal.

Continuing Claims

A good measure of how much unemployment is growing.

Continued claims, also referred to as insured unemployment, is the number of people who have already filed an initial claim and who have experienced a week of unemployment and then filed a continued claim to claim benefits for that week of unemployment. Continued claims data are based on the week of unemployment, not the week when the initial claim was filed

https://fred.stlouisfed.org/series/CCNSA

A good measure of how much unemployment is growing

Unemployment rates, year-over-year

Both the headline unemployment and U-6 number changes are similar. During the upswing on the cycle it does look like the headline number falls faster than U-6

The second derivative of the unemployment rate does have zero crossings near the middle point of a recession. This would make it a helpful buy signal for the trading strategy.

Unemployment rates, similar periods

Historically the last two years of record low unemployment appear most similar to the 1971-1973 time frame. Just before inflation took off.

Unemployment rates, U-6 and headline number.

Let’s also take a look at the total unemployed, U-6. It continues to fall as the headline number stabilizes as people return to the work force. An indicator the cycle is beginning to top out.

Difference between U6 and U3 to see how close the economy is getting to full employment.

Unemployment and market bottoms

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Initial jobless claims

We will also take a look at initial jobless claims, this should start to rise just before the unemployment rate.

It looks like the jobless claim tend to peak more towards the end of the recession. It does not seem to be as strong of a sell indicator as the U-3 rate.

Jobless claims have a seasonal component to them. One way to reduce this effect is to calculate year over year growth. That helps some, the peaks seem to be more closely aligned with the middle to end of recessions.

Take a closer look at recent data

Take a look at the percentage of the population looking for work

A bit more recent trend

Unemployment Level

ADP data here. comes out before the official numbers.

Look at the year-over-year change in ADP.

ADP data divided by the population

Payrolls

Look at the BLS data on payrolls. Check the NSA series, then we will look at YoY data.

Hours worked

Sparked by an article at Mises (https://mises.org/wire/how-alexandria-ocasio-cortez-misunderstands-american-poverty), take a look at average weekly hours

The time series is pretty lumpy, plot the YoY change

A more recent look at average weekly hours of production

Industrial Production

Industrial production is also known to fall during an economic downturm, let’s take a look at some of the data from the FRED on industrual production. It does seem to peak prior to a recession so let’s smooth and look at the derivative as it might be a good indicator as well.

Industrial production over the last ten years or so

The derivative isn’t bad, but it sometimes crosses zeros well into a recession. That is less helpful as either a buy or sell indicator. A better measure might year over year (YoY) change.

The year over year change has a similar appearance. The low values at the beginning make the year over year values larger than the more recent values. Seems like it will rank low a reliable indicator.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 12)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 50)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail Sales

Retail sales, aggregate

Retail sales also change during recession. As the plot below shows, it seems to follow the trend of industrial production. It might be too strongly correlated to add much to the model. The will be examined in the correlation section.

The derivative of retail sales is a little more erratic than is was the industrial products. Looks like it might be helpful to include in the model as well.

Retail sales, aggregate year-over-year

Take a look at year-over-year changes

Retail sales and unemployment correlations

Let’s see how that looks on year over year basis. Interesting to compare to unemployment rates there appears to a correlation over the long term.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

There is some similarity. The rolling correlation shows the inverse relationship prior to a recession.

datay1 <- "RSALESAGG_YoY"
ylim1 <- c(-12.5, 7.5)

datay2 <- "UNEMPLOY_YoY"
ylim2 <- c(-30, 100)

dtStart <- as.Date("1jan1970","%d%b%Y")

w <- 180
corrName <- calcRollingCorr(dfRecession,df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail sales correlation and industrial production

Industrial production and retail sales look very similar so the plot below shows the 360 correlation. The corerlation does tend to fall around a recession, although 2008 was so bad that they both fell together. Not sure if it is that useful.

datay1 <- "INDPRO"
ylim1 <- c(40, 125)

datay2 <- "RSALESAGG"
ylim2 <- c(100000, 200000)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

It is interesting to see the strong correlation; however, I suspect this is due to more to the shape of the trends. How do the YoY correlations look? They are a little less correlated, probably better to use in the machine learning later.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 20)

datay2 <- "RSALESAGG_YoY"
ylim2 <- c(-20, 20)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Advance Retail Sales

This is an advanced estimate of the retail sales value.

Also take a look at year over year

Retail sales and the labor market

Income

Real Personal Income

Real Personal Income (Excluding Transfer, Annual)

During a recession real personal income falls. In the plot the peaks can be seen prior to each recession.

datay <- "W875RX1"
ylim <- c(3000, 15000)
plotSingleQuickModern(datay, ylim)

The features we are interested in are the peaks and valleys so we’ll use the derivative to get to those. Interesting, there is usually a first zero crossing before a recession and a second during or just after the recession.

Real personal income might have some seasonal variance, but it seems the year over year change tells the same story.

Price and cost measures

This section shows price and cost measures.

Two commonly used indexes are the CPI (consumer price index) and PPI (producer price index). CPI tries to show final prices paid for goods and services by urban U.S. consumers. This index includes sales tax and imports. The PPI attempts to reflect the prices paid at all stages of production, including goods and services purchases as inputs as well as goods and services purchased by consumers from retail and producer sellers. The PPI does not include imports or sales tax. The CPI reflects all rebates and financing plans wherease the PPI reflects only those rebate and financing plans provided by the producer. For example if an automotive manufacturer offers a rebate of $500 and the dealer offers an additional rebate of $500 then the PPI would reflect only the automotive manufacturer rebate, but the CPI would reflect both rebates.

Sources; https://www.bls.gov/opub/hom/pdf/cpihom.pdf and https://www.bls.gov/opub/hom/pdf/ppi-20111028.pdf.

Consumer price index

What does CPI look like?

datay <- "CPIAUCSL"
ylim <- c(0, 300)
plotSingleQuickModern(datay, ylim)

Check out the YoY growth

datay <- "CPIAUCSL_YoY"
ylim <- c(-2, 15)
plotSingleQuickModern(datay, ylim)

CPI to PPI

Suggested by Charlie, it can be helpful to look at the relationship between producer prices and consumer prices.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Producer Price Index (Commodities)

Commodities

Basket

Take a look at some trends of baskets of commodities.

Crude oil

Look at a trend of West Texas Intermediate (WTI)

This is ticker data from yahoo

Take a look at both WTI and Brent crude.

Real price of crude using producer price index for commodities

Gold

As risks increase investors often flock to safe haven assets like gold. An up-tick in prices can indicate investor uncertainty. This can be seen in the nominal price plot around 1980 and again in 2007.

This plots out the real price of gold by two different deflators. PPI corrected price is a little higher, to be expected since CPI also includes the effects of sales tax and imports. The spike in 1980 is especially pronounced in this series.

See how nominal and real prices look year over year. From the long-term view seems like there is little difference in the three series. Although not shown, even over the near-term there is little difference in the series.

See how gold correlates with the VIX. Both gold and VIX should respond to investor axiety, but it doesn’t look like it correlates very well.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 245 rows containing non-finite values (stat_smooth).

Copper

Dr. Copper has a reputation as an indicator of economic malaise, but it does not seem to have much of a correlation with the recessions. The series below is from CME via Quandl. It has a lot of data so I am also looking at the smoothed version.

Copper is one of the commodities in the PPI so it is a bit of a proxy for how copper is doing relative to the basket of commodities.

The change in prices, year over year, do generally peak prior to a recession. The time and shape of this peak varies, but it still might be helpful. A couple of the large troughs do seem to correlate with the end of the recession. Likely this is because industrial production has also fallen.

There is some correlation between copper and the smooth recession initiator, especially at the end of the recession.

Might be easier to see correlation in a dot plot format.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 342 rows containing non-finite values (stat_smooth).

This is a legacy series from FRED. It has not been updated in a couple of years so I am assuming it will go away.

Oil Services

Amazing events in the first half of 2020, take a look at those

See how the players are doing

Federal Reserve

The federal reserve has an impact on the economy, here are some data series relating to that.

Little bit closer

datay <- "WALCL"
ylim <- c(0, 8500)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Federal Reserve Reverse Repo Agreements

Compare liabilities to reverse repo trends

Spiky, might be easier to look at year-over-year

Normalized by GDP

datay <- "WLRRAL.by.GDP"
ylim <- c(0, 4)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Overnight Bank Funding Rate

“The overnight bank funding rate is calculated using federal funds transactions and certain Eurodollar transactions. The federal funds market consists of domestic unsecured borrowings in U.S. dollars by depository institutions from other depository institutions and certain other entities, primarily government-sponsored enterprises, while the Eurodollar market consists of unsecured U.S. dollar deposits held at banks or bank branches outside of the United States. U.S.-based banks can also take Eurodollar deposits domestically through international banking facilities (IBFs). The overnight bank funding rate (OBFR) is calculated as a volume-weighted median of overnight federal funds transactions and Eurodollar transactions reported in the FR 2420 Report of Selected Money Market Rates. Volume-weighted median is the rate associated with transactions at the 50th percentile of transaction volume. Specifically, the volume-weighted median rate is calculated by ordering the transactions from lowest to highest rate, taking the cumulative sum of volumes of these transactions, and identifying the rate associated with the trades at the 50th percentile of dollar volume. The published rates are the volume-weighted median transacted rate, rounded to the nearest basis point.” https://www.newyorkfed.org/markets/obfrinfo.

Secured Overnight Financing Rate

“The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by Treasury securities. The SOFR includes all trades in the Broad General Collateral Rate plus bilateral Treasury repurchase agreement (repo) transactions cleared through the Delivery-versus-Payment (DVP) service offered by the Fixed Income Clearing Corporation (FICC), which is filtered to remove a portion of transactions considered “specials” " https://apps.newyorkfed.org/markets/autorates/sofr

Take a look at the variation (99th - 1st percentile)

Reserve Balances with Federal Reserve Banks

Hard to get a sense of these series in the absolute. Take a look relative to GDP.

By double entry book-keeping reserves+loans (assets) = deposit (liabilities). Does that really work?

Correlation Between Reserves and Total Loans

As reserves increase there should be less lending. That correlation generally holds.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Did the reserve balances increase after the 2016 and 2018 drops? Not in the same way. There are some relationships between the equities market and the reserves though.

Explicitly correlate reserve balances and total loans. It is a weak and noisy correlation.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 990 rows containing non-finite values (stat_smooth).

Interest on excess reserves

Monetary Base

Currency trend, base

This used to trend along with GDP. It doesn’t anymore.

Money supplies

Basic currency trend (currency component of M1)

datay <- "WCURRNS_YoY"
dtStart = as.Date('1980-01-01')
ylim <- c(0, 17)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

datay <- "WCURRNS_YoY"
dtStart = as.Date('2000-01-01')
ylim <- c(0, 20)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

The rate of change of money supply could be an indicator of a recession. Let’s see how that compares.

Intervention in the repo market

The federal reserve provides liquidity to the repo market, summary of that action

European central bank

The European central band (ECB) has taken a different path compared to the US Federal Reserve bank.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Federal Debt

The government is a big driver of the economy, let’s see what it is doing in the debt markets.

datay <- "GFDEBTN"
ylim <- c(0, 28000000)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_Log"
ylim <- c(12, 18)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_YoY"
ylim <- c(-10, 25)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal debt as percent GDP

datay <- "GFDEGDQ188S"
ylim <- c(30, 150)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal deficit as percent GDP

datay <- "FYFSGDA188S"
ylim <- c(-30, 5)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Charlie Hatch has a nice format of deficit versus debt:

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Nonfinancial Corporate Business Debt

What about Nonfinancial corporate business and debt securities? Hopefully this doesn’t follow the business loan trends.

That is crazy steep. Time for a log format, see if that brings out the peaks and troughs. That’s a litte better, it looks like there might be a change in slope prior to the recessions.

The derivative doesn’t seem to be much help. There is not much correlation between the zero crossings and the NEBR recessions.

Debt cycle

This analysis roughly follows the ideas in Big Debt Crises book by Ray Dalio.

Total loans

One business cycle theory describes recessions as a market adjustment to mis-allocated assets, often fueled by an credit expansion. That makes the volume of loans an interesting feature to look at. In the presentation of data it looks like the great recession had the largest impact.

Plotting the year over year growth rate helps pull out those small changes in the early years in the data. Peaks can be seen prior to most recessions.

Zoom in to the last couple of decades

As long term interest rates rise, loans should start to tick down. To check this, the total loans and 10 to 1 year spreads are plotted. This is generally the trend observed.

There is a good correlation between these two variables. This next section plots that correction explicitly.

Total loans as percent of GDP

This is the total loans. I think the picture is too broad to point to a specific sector of the economy. The debt burden assumes interest rates are tied to the 10-year treasury: (TOTLNNSA * DGS10) / 100

Commercial and industral loans

Business loans should slow before the recession (a contraction in credit as rates rise).

Commercial and industrial loans as percent of GDP and and income

Look at business debt normalized by GDP over the entire time series. This ratio often peaks at the mid-point of a recession.

https://www.wsj.com/articles/this-isnt-your-fathers-corporate-bond-market-11590574555

“Bonds are behaving more like bank debt, which tends to remain stable or even increase at the onset of recessions, as lenders keep distressed clients afloat—and only later turn off the taps. This was confirmed by a recent report from the Bank for International Settlements. It also found a tight link between this lending cycle and the “real” economy’s booms and busts."

I assume that interest is related to the 10-year treasure: (TOTCINSA * DGS10) / 100

Farm loans

See how the farming sector is fairing.

Real estate loans

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

This gives a big picture, but makes it hard to connect the loans with the income needed to cover those loans. In the next section, loans will be broken up by commercial and residential.

Real Estate (Residential)

In absolute terms the mortgages have increased, but it does not appear to be out of line with the overall economy.

Normalized by GDP it is easier to see the peak in 2008 and that loan levels appear reasonable at the commercial banks.

Maybe the GSE’s are making loans. Take a look at the total mortgages from Z.1 as a percentage of GDP. That does not look too far off trend (ignoring that peak in 2008).

I am assuming that personal income is paying for the mortgages.

Real estate (residential) as percent of GDP and and income

## Warning: Removed 1 rows containing missing values (geom_text).

Consumer loans

Focusing on the consumer sector the growth in debt and incomes can be directly compared. Personal income, as a percent of GDP, remains nearly constant. It is not uncommon for the personal income to rise prior to a recession. Likely this reflect increasing asset prices and market returns. Also interesting to see the loans pick up after interest rates dropped in 1982.

Consumer loans as percent of GDP and and income

Take a closer look since the 2008 recession. Looks like loans are starting to slow as the interest burden rises and incomes remain stable. There are some anomolies in the A065RC1A027NBEA data series because it only updates onces a year. the PI series updates once a month but is noisier and seasonally adjusted. It also shows incomes rising in the middle of the 2008 recession, which doesn’t seem to be accurate.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_text).

Repo market

This market went through some stress in 2008, it is happening again so setup some plots to watch it.

Nonfincial corporate business security repo asset level

Bonds

T-Bills and Yield Curve

Speaking of loans, interest rates also play into this. This analysis will focus on treasure bills. The 3-month is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 2.5)
dtStart = as.Date('2017-01-01')
p1 <- plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Check out LIBOR and fed funds rate

The 1-year is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "DGS10"
datay.aux <- "TNX.Close"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Close in, the trend towards inversion be more easily seen. I am also comparing data from the CBOE as well as FRED.

Bond yields are a good proxy for interest rates. As rates rise the theory goes that loans should decrease (inverse correlation).

And a longer window

The yield curve (30 year bond rate minus the 10 year bond rate) may not be a good recession indicator, but a collapse is not good (https://blogs.wsj.com/moneybeat/2018/04/30/theres-more-than-one-part-of-the-yield-curve-getting-flatter/).

The yield curve (10 year bond rate minus the 1 year bond rate) seems to a good indicator of an oncoming recession. It could be a buy indicator by itself.

More recent data

Just the last 24 months or so.

Plot the 10 Year to 3 month over a few decades to see what the outling cases look like

The last two year compare favorably with the period around the 2015-2016 turndown, driven primarily by slowing of the Chinese GDP. Not a debt-driven cycle.

This plot format was suggested by a mises.org article (https://mises.org/wire/yield-curve-accordion-theory), but they only went back to 1988. The date seemed arbitrary so I went back further in time.

Take a look at more recent data

Try looking at a 1-year average of the above time series

High quality bonds

datay <- "AAA"
ylim <- c(2.5, 10)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds to 10-year treasury

High quality bonds long-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('1967-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds near-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('2007-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High yield spread

“This data represents the Option-Adjusted Spread (OAS) of the ICE BofAML US Corporate A Index, a subset of the ICE BofAML US Corporate Master Index tracking the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. This subset includes all securities with a given investment grade rating A. The ICE BofAML OASs are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond‚Äôs OAS, weighted by market capitalization. When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.”

• ICE Benchmark Administration Limited (IBA), ICE BofAML US Corporate A Option-Adjusted Spread [BAMLC0A3CA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLC0A3CA, July 4, 2019.

datay <- "BAMLC0A3CA"
ylim <- c(0, 7)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Municipal bond market

Suggest by a WSJ article, change in volume for high-risk muni’s. Doesn’t look like there is much too it yet.

https://www.wsj.com/articles/risky-municipal-bonds-are-on-a-hot-streak-11558949401?mod=hp_lead_pos3

datay <- "HYMB.Close"
ylim <- c(40, 62)
dtStart = as.Date('2011-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

datay <- "HYMB.Volume"
ylim <- c(0, 1750000)
p1.vol <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


datay <- "GSPC.Open"
datay_aux <- "GSPC.Close"
ylim <- c(1500, d.GSPC.max )
p2 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


grid.arrange(p1,
             p1.vol,
             p2,
             ncol = 1,
             top = "High Yield Muni's and S&P Price")

Total Loans and yield curve correlation

This relationship was suggest by Charlie and it is an interesting one. As the yield curve flattens (10-year and 1-year rates converge), total loans grow. The generalization is not always accurate, but it does fit.

## `geom_smooth()` using formula 'y ~ x'

I wanted to see how this looked compared to the 3 month

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 282 rows containing non-finite values (stat_smooth).

Consumer loans and yield curve correlation

Compared to business loans, consumer loans seem to have to response to the 10Y to 3M yield curve.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 311 rows containing non-finite values (stat_smooth).

Business loans and yield curve correlation

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 103 rows containing non-finite values (stat_smooth).

That’s pretty good correlation. Let’s see what the rolling correlation looks like.

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 720
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

One other items, let’s see how loans do versus the federal funds rate

## `geom_smooth()` using formula 'y ~ x'

EIA Information

Petroleum

Consumption/Sales > Refiner Motor Gasoline Sales Volumes > by Product > Motor Gasoline > by Area > U.S.

Total Energy

Crude Oil and Natural Gas Resource Development > Crude Oil and Natural Gas Drilling Activity Measurements

Baker Hughes Rig Count

BEA Supplemental Estimates, Motor Vehicles

Definitions

Autos–all passenger cars, including station wagons.
Light trucks–trucks up to 14,000 pounds gross vehicle weight, including minivans and
sport utility vehicles. Prior to the 2003 Benchmark Revision light trucks were up to 10,000 pounds.
Heavy trucks–trucks more than 14,000 pounds gross vehicle weight.
Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds.
Domestic sales–United States (U.S.) sales of vehicles assembled in the U.S., Canada, and Mexico.
Foreign sales–U.S. sales of vehicles produced elsewhere.
Domestic auto production–Autos assembled in the U.S.
Domestic auto inventories–U.S. inventories of vehicles assembled in the U.S., Canada, and Mexico.

TAble 6 - Light Vehicle and Total Vehicle Sales

Auto sales

A WSJ article suggested that auto sales might be a good indicator so bring that to the mix. It does have troughs that correlate with recessions

There might be some seasonal variance in the auto sales so lets take a look at the year over year. The data is pretty noisy, it probably will not make a very good indicator.

BEA Gross Domestic Product

Data in this section come from the Bureau of Economic Analysis.

Table 1.1.5. Gross Domestic Product

[Billions of dollars] Seasonally adjusted at annual rates

A191RC: Gross Domestic Product - Line 1

GDP numbers tend to lag so this series is truly an afterthought. But it does have some correlation with the recessions.

GDP does not reflect the capacity of the economy nor the efficiency. Shrinking capacity and lower prices at constant volumes would indicate improvements in effeciency/productivity which is good for the economy, but does not move the GDP upward.

Looks like the year over year change on the GDP should correlate well with unemployment.

Table 1.1.9. Implicit Price Deflators for Gross Domestic Product

[Index numbers, 2012=100] Seasonally adjusted

A191RD: Gross Domestic Product - Line 1

This is GDP price deflator series.

GDP normalized by CPI

Normalize GDP by CPI

Economic yield curve (GDP to 1-year treasury)

GDP versus the yield on the 1-year. This series was prompted by an article suggesting that the “economic yield curve” should be used to indicate a recession rather than an inverted yield curve. Less of indicator and more of concurrent confirmation of recession. Not sure why they would be related either.

Economic yield curve (GDP to 3-month treasury)

Same idea as above, but applied the 3-month treasury.This one has fewer false triggers, but is not as helpful as 10Y to 3M spread in predicting a recession.

A824RC: National defense Federal Gov’t Expenditures - Line 24

U.S. Bureau of Economic Analysis, Federal Government: National Defense Consumption Expenditures and Gross Investment [FDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FDEFX, April 6, 2021.

A825RC: Nondefense Federal Gov’t Expenditures - Line 25

U.S. Bureau of Economic Analysis, Federal Government: Nondefense Consumption Expenditures and Gross Investment [FNDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FNDEFX, April 6, 2021.

Table 6.16D. Corporate Profits by Industry

Select series from Table 6.16D

A051RC: Corporate profits with inventory and capital consumption adjustment

From BEA’s documentation (https://www.bea.gov/media/5671):

“BEA’s featured measure of corporate profits — profits from current production - provides a comprehensive and consistent economic measure of the income earned by all U.S. corporations. As such, it is unaffected by changes in tax laws, and it is adjusted for nonreported and misreported income. It excludes dividend income, capital gains and losses, and other financial flows and adjustments, such as deduction for “bad debt.” Thus, the NIPA measure of profits is a particularly useful analytical measure of the health of the corporate sector. For example, in contrast to other popular measures of corporate profits, the NIPA measure did not show the large run-up in profits during the late 1990s that was primarily attributable to capital gains.

Profits after tax with IVA and CCAdj is equal to corporate profits with IVA and CCAdj less taxes on corporate income. It provides an after-tax measure of profits from current production."

Data is Line 1 of Table 6.16D

A053RC: Corporate profits without inventory and capital consumption adjustment

Profits look a bit flat over the last several years in this series.

Table 2.6. Personal Income and Its Disposition, Monthly

Billions of dollars; months are seasonally adjusted at annual rates.

A065RC Personal Income - Line 1

BEA Account Code: A065RC

Personal income is the income that persons receive in return for their provision of labor, land, and capital used in current production and the net current transfer payments that they receive from business and from government.25 Personal income is equal to national income minus corporate profits with inventory valuation and capital consumption adjustments, taxes on production and imports less subsidies, contributions for government social insurance, net interest and miscellaneous payments on assets, business current transfer payments (net), current surplus of government enterprises, and wage accruals less disbursements, plus personal income receipts on assets and personal current transfer receipts. A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Income [PI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PI, July 11, 2019.

DPCERC: Personal consumption expenditures (PCE) - Table 2.1, Line 29

BEA Account Code: DPCERC Personal consumption expenditures (PCE) is the primary measure of consumer spending on goods and services in the U.S. economy. 1 It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. PCE shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption. -https://www.bea.gov/system/files/2019-12/Chapter-5.pdf

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures [PCE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCE, June 12, 2020

DPCERG: Personal consumption expenditures Price Index (PCEPI) - Table 2.1, Line 29

BEA Account Code: DPCERG The gross domestic product price index measures changes in prices paid for goods and services produced in the United States, including those exported to other countries. Prices of imports are excluded. The gross domestic product implicit price deflator, or GDP deflator, basically measures the same things and closely mirrors the GDP price index, although the two price measures are calculated differently. The GDP deflator is used by some firms to adjust payments in contracts.

The gross domestic purchases price index is BEA’s featured measure of inflation for the U.S. economy overall. It measures changes in prices paid by consumers, businesses, and governments in the United States, including the prices of the imports they buy.

BEA’s closely followed personal consumption expenditures price index, or PCE price index, is a narrower measure. It looks at the changing prices of goods and services purchased by consumers in the United States. It’s similar to the Bureau of Labor Statistics’ consumer price index for urban consumers. The two indexes, which have their own purposes and uses, are constructed differently, resulting in different inflation rates.

The PCE price index is known for capturing inflation (or deflation) across a wide range of consumer expenses and for reflecting changes in consumer behavior. For example, if the price of beef rises, shoppers may buy less beef and more chicken. Also, BEA revises previously published PCE data to reflect updated information or new methodology, providing consistency across decades of data that’s valuable for researchers. The PCE price index is used primarily for macroeconomic analysis and forecasting. -https://www.bea.gov/resources/learning-center/what-to-know-prices-inflation

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures: Chain-type Price Index [PCEPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEPI, April 25, 2021.

A072RC: Personal Savings Rate - Line 35

Consumers tend to pull down their savings rates as unemployment decreases and market conditions improve. This series has tended to be unreliable due to the size of revisions during the comprehensive update carried out by the BEA. The last update on this series moved the rate from 4.2 to 6.7 percent.

(https://www.bloomberg.com/news/articles/2018-07-27/americans-have-been-saving-much-more-than-thought-new-data-show)

BEA Account Code: A072RC Personal saving as a percentage of disposable personal income (DPI), frequently referred to as “the personal saving rate,” is calculated as the ratio of personal saving to DPI. Personal saving is equal to personal income less personal outlays and personal taxes; it may generally be viewed as the portion of personal income that is used either to provide funds to capital markets or to invest in real assets such as residences.(https://www.bea.gov/national/pdf/all-chapters.pdf) A Guide to the National Income and Product Accounts of the United States (NIPA).

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PSAVERT, July 9, 2019.

Take a closer look at the last decade

The relationship between personal savings and unemployment (U-3) can be better visualized with a scatter plot

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 189 rows containing non-finite values (stat_smooth).

The fit does not explain most of what is in the plot. Lets take a look at the rolling correlation.

datay1 <- "UNRATE"
ylim1 <- c(2, 12)

datay2 <- "PSAVERT"
ylim2 <- c(0, 35)

dtStart <- as.Date("1jan1985","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Personal savings to household net worth

A relationship between personal savings and household networth can be seen in a scatter plot. This was suggested by a WSJ article (https://blogs.wsj.com/dailyshot/2018/02/23/the-daily-shot-reasons-for-declining-u-s-household-savings-rate/).

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 216 rows containing non-finite values (stat_smooth).

U.S. Census Bureau

U.S. International Trade in Goods and Services (FT900)

U.S. Bureau of Economic Analysis and U.S. Census Bureau, U.S. Imports of Goods by Customs Basis from China [IMPCH], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IMPCH, October 5, 2019.

New Houses Sold and For Sale by Stage of Construction and Median Number of Months on Sales Market

Read an article suggesting that housing sales and sales growth could be useful. FRED only has new home data so start there.

datay <- "HSN1FNSA"
ylim <- c(0, 200)
dtStart = as.Date('1964-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA"
ylim <- c(0, 600)
p2 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA.minus.HSN1FNSA"
ylim <- c(0, 600)
p3 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

grid.arrange(p1,
             p2,
             p3,
             ncol = 1,
             top = "New Housing Sales")

New housing yoy

New Privately-Owned Housing Units Authorized in Permit-Issuing Places

As provided by the Census, start occurs when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when this excavation begins. Beginning with data for September 1992, estimates of housing starts include units in structures being totally rebuilt on an existing foundation.

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Housing Starts: Total: New Privately Owned Housing Units Started [HOUST], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUST, June 13, 2020.

Take a look at privately owned starts

New Privately-Owned Houses Sold and For Sale

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Median Sales Price of Houses Sold for the United States [MSPUS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MSPUS, June 13, 2020.

Finally, take a look at starts times the median price

Durable Goods

Suggested Citation: U.S. Census Bureau, Manufacturers’ New Orders: Durable Goods [UMDMNO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UMDMNO, April 26, 2021.

Durable goods, not seasonally adjusted, divided by GDP

Durable goods, seasonally adjusted, divided by GDP

Federal reserve board H.8: Assets and Liabilities of Commercial Banks in the United States

Page 4: Not Seasonally adjusted, billions of dollars

Commercial and industrial loans, all commercial banks - Line 10

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Commercial and Industrial Loans, All Commercial Banks [BUSLOANS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BUSLOANS, July 11, 2019.

Taking a look at the difference in SA and NSA series. Seasonal adjustments do vary, but do not seem to be related to recessions.

The raw series is just too steep for any kind of machine learnine. This needs to be converted to log scale.

That’s a little better, let’s see what the smoothed derivative looks like.

That is odd…looks like this doesn’t cross zero unless we are getting close to, or into, a recession. The year over year tells about the same story. Might be a good indication of the end of a recession.

Consumer loans, all commercial banks - Line 20

Suggested Citation: Board of Governors of the Federal Reserve System (US), Consumer Loans, All Commercial Banks [CONSUMERNSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CONSUMERNSA, July 11, 2019.

That spike in consumer loans is due to

“April 9, 2010 (Last revised September 23, 2011): As of the week ending March 31, 2010, domestically chartered banks and foreign-related institutions had consolidated onto their balance sheets the following assets and liabilities of off-balance-sheet vehicles, owing to the adoption of FASB’s Financial Accounting Statements No. 166 (FAS 166),”Accounting for Transfers of Financial Assets," and No. 167 (FAS 167), “Amendments to FASB Interpretation No. 46(R).”

This included a consumer loans, credit cards and other revolving plans change of $321.9B. That was a lot of off-balance-sheet bank assets.

Deposits, All Commercial Banks, all commercial banks - Line 34

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Deposits, All Commercial Banks [DPSACBW027SBOG], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DPSACBW027SBOG, May 14, 2020.

Federal reserve board Z.1: Financial Accounts of the United States

From the FRED website (https://fred.stlouisfed.org/release?rid=52):

"The Financial Accounts (formerly known as the Flow of Funds accounts) are a set of financial accounts used to track the sources and uses of funds by sector. They are a component of a system of macroeconomic accounts including the National Income and Product accounts (NIPA) and balance of payments accounts, all of which serve as a comprehensive set of information on the economy’s performance.(1) Some important inferences that can be drawn from the Financial accounts are the financial strength of a given sector, new economic trends, changes in the composition of wealth, and development of new financial instruments over time.(1)

Sectors are compiled into three categories: households, nonfinancial businesses, and banks. The sources of funds for a sector are its internal funds (savings from income after consumption) and external funds (loans from banks and other financial intermediaries). (1) Funds for a given sector are used for its investments in physical and financial assets. Dividing sources and uses of funds into two categories helps the staff of the Federal Reserve System pay particular attention to external sources of funds and financial uses of funds.(2) One example is whether households are borrowing more from banks—or in other words, whether household debt is rising. Another example might be whether banks are using more of their funds to provide loans to consumers. Transactions within a sector are not shown in the accounts; however, transactions between sectors are.(2) Monitoring the external flows of funds provides insights into a sector’s health and the performance of the economy as a whole.

Data for the Financial accounts are compiled from a large number of reports and publications, including regulatory reports such as those submitted by banks, tax filings, and surveys conducted by the Federal Reserve System.(2) The Financial accounts are published quarterly as a set of tables in the Federal Reserve’s Z.1 statistical release.

1. Teplin, Albert M. “The U.S. Flow of Funds Accounts and Their Uses.” Federal Reserve Bulletin, July 2001; http://www.federalreserve.gov/pubs/bulletin/2001/0701lead.pdf.
2. Board of Governors of the Federal Reserve System. “Guide to the Flow of Funds Accounts.” 2000, http://www.federalreserve.gov/apps/fof/."

L.102 Nonfinancial Business

FL102051003.Q: Nonfinancial corporate business; security repurchase agreements; asset

Asset level of nonfinancial business security repo agreements. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL102051003&t=

L.214 Loans

FL894123005.Q: All sectors; total loans; liability

Sum of domestic financial sectors, all sectors, total mortgages, and households/non-profits. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL894123005&t=L.107&bc=L.107:FL793068005&suf=Q

FL793068005.Q: Domestic financial sectors; depository institution loans n.e.c.; asset

Sum of Monetary authority; depository institution loans n.e.c.; asset and Private depository institutions; depository institution loans n.e.c.; asset. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793068005&t=L.214&suf=Q

FL893169005.Q: All sectors; other loans and advances; liability

Sum of finance, government, and chartered institutions asset levels. https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893169005&t=L.214&suf=Q

FL893065005.Q: All sectors; total mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065005&t=L.214&suf=Q

FL893065105.Q: All sectors; home mortgages; asset

https://www.federalreserve.gov/apps/fof/DisplayTable.aspx?t=L.214

FL893065405.Q: All sectors; multifamily residential mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065405&t=L.214&suf=Q

FL893065505.Q: All sectors; commercial mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065505&t=L.214&suf=Q

FL893065603.Q: All sectors; farm mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065603&t=L.214&suf=Q

FL153166000.Q: Households and nonprofit organizations; consumer credit; liability

federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL153166000&t=L.214&suf=Q

B.101 Balance Sheet of Households and Nonprofit Organizations

FL152000005.Q: Households and nonprofit organizations; total assets, Level

string.source ID: FL152000005.Q.

FL152090006.Q: Household Net Worth as Percentage of Disposable Personal Income

string.source ID: FL152090006.Q. Household networth tends to fall as a recession start.

Productivity Yield Curve

GDP versus productivity

Manufacturing output and employees

Not sure if these relates to a recession, but fascinating to see how output and employees change with time.

datay <- "OUTMS"
ylim <- c(60, 120)
dtStart = as.Date('1987-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "MANEMP"
ylim <- c(10000, 20000)
dtStart = as.Date('1948-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "PRS30006163"
ylim <- c(40, 120)
dtStart = as.Date('1986-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Shipping volumes might be helpful in determining state of the economy.

datay <- "FRGSHPUSM649NCIS"
ylim <- c(0.8, 1.4)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "FRGSHPUSM649NCIS_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Freight, loosely, moves inversely to the trade deficit.

datay <- "BOPGTB_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

World bank air transportation. Only updated annually so less usefull, but interesting reference to above.

datay <- "WWDIWLDISAIRGOODMTK1"
ylim <- c(0, 250000)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Gross private domestic investment

Spending most certainly tips down prior to a recession. The gross private domestic investment data series, plotted in log format below, show how private investment pulls back prior to recessions.

The change in direction is a little easier to see if the derivative is plotted, first YoY then the smoothed derivative

Velocity

Productivity

Date range to match census data

PMI

Industrial Production

This is a look at manufacturing industrial production. The yoY change should be a leading indicator of unemployment.

Housing

Take a look at housing starts. These can drop as rates rise.

Case-schiller price index

Population data

Many of the economic series can be better understood if normalized by population. Basic population and worker data from FRED.

Population to GDP

Look at GDP divided by CPI per person. It flattens and even dips a little prior to a recession. Might be worth looking at the derivative of this series.

That is worth a closer look

datay1 <- "GDPBYCPIAUCSLBYPOPTHM_SmoothDer"
ylim1 <- c(-5, 5)

datay2 <- "RecInit_Smooth"
ylim2 <- c(0, 1)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Correlation Study

Detailed correlations are explored above. Before concluding, let’s take a look at some overall correlation values to see if anything pops out.

Commodities

As mentioned above, copper, year over year, has some correlation with the recession initiation. It could be useful.

GDP Series

GDP, normalized first by CPI and then by population, looks like it migh correlate inversely with the recession indicators

Financials

Let’s see where we are so far. The correlation plot confirms some of the speculation above. The S&P 500 (GSPC.Open) is well correlated with industrial production (INDPRO), business loans (BUSLOANS), total loans (TOTLNNSA) , and nonfinancial corporate business debt (NCBDBIQ027S).

In this case, I want and indicator that rises prior to a recession. It looks like the unemployment rate (UNRATE), real personal income (W875RX1), and the yield curve (DGS10TO1) are all inversely correlated with the recession initiation indicator.

I thought the modified recession initiation would be a harder match, but there are quite a few correlated variables. Lets take a look at some of those in more detail

Complete list of symbols

Since it is tedious to do this one at a time, all the symbols were entered into a data frame, loaded, and aggregated together in a single xts object.

This is the complete list of symbol names and sources used in the project.